Cigarette smoke is a representative source of toxic chemical exposures to humans, and the adverse consequences of cigarette smoking are mediated by its effect on both neuronal and immune–inflammatory systems. Cigarette smoking also is a major risk factor for intestinal disorders, such as Crohn’s disease and peptic ulcer. On the other hand, cigarette smoking is protective against developing ulcerative colitis. The effects of cigarette smoking on intestinal disorders include changes in intestinal irrigation and microbiome, increases in permeability of the mucosa, and impaired mucosal immune responses. However, the underlying mechanism linking cigarette smoking with intestinal microbiota dysbiosis is largely unknown. In this communication, we first review the current knowledge about the mechanistic interaction between cigarette smoke and intestinal microbiota dysbiosis, which include the likely actions of nicotine, aldehydes, polycyclic aromatic hydrocarbons, heavy metals, volatile organic compounds and toxic gases, and then reveal the potential mechanisms of the lung–gut cross talk and skin-gut cross talk in regulating the balance of intestinal microbiota and the interrelation of intestinal microbiota dysbiosis and systemic disorders.

Immunotherapy utilizing chimeric antigen receptor T cell (CAR-T) therapy holds promise for hematologic malignancies, however, response rates and associated immune-related adverse effects widely vary among patients. Here we show, by comparing diversity and composition of the gut microbiome during different CAR-T therapeutic phases in the clinical trial ChiCTR1800017404, that the gut flora characteristically differs among patients and according to treatment stages, and might also reflect patient response to therapy in relapsed/refractory multiple myeloma (MM; n = 43), acute lympholastic leukemia (ALL; n = 23) and non-Hodgkin lymphoma (NHL; n = 12). We observe significant temporal differences in diversity and abundance of Bifidobacterium, Prevotella, Sutterella, and Collinsella between MM patients in complete remission (n = 24) and those in partial remission (n = 11). Furthermore, we find that patients with severe cytokine release syndrome present with higher abundance of Bifidobacterium, Leuconostoc, Stenotrophomonas, and Staphylococcus, which is reproducible in an independent cohort of 38 MM patients. This study has important implications for understanding the biological role of the microbiome in CAR-T treatment responsiveness of hematologic malignancy patients, and may guide therapeutic intervention to increase efficacy. The success rate of CAR-T cell therapy is high in blood cancers, yet individual patient characteristics might reduce therapeutic benefit. Here we show that therapeutic response in MM, ALL and NHL, and occurrence of severe cytokine release syndrome in multiple myeloma are associated with specific gut microbiome alterations.

This study aims to characterize the gut microbiota in depressed patients with bipolar disorder (BD) compared with healthy controls (HCs), to examine the effects of quetiapine treatment on the microbiota, and to explore the potential of microbiota as a biomarker for BD diagnosis and treatment outcome. Analysis of 16S‐ribosomal RNA gene sequences reveals that gut microbial composition and diversity are significantly different between BD patients and HCs. Phylum Bacteroidetes and Firmicutes are the predominant bacterial communities in BD patients and HCs, respectively. Lower levels of butyrate‐producing bacteria are observed in untreated patients. Microbial composition changes following quetiapine treatment in BD patients. Notably, 30 microbial markers are identified on a random forest model and achieve an area under the curve (AUC) of 0.81 between untreated patients and HCs. Ten microbial markers are identified with the AUC of 0.93 between responder and nonresponder patients. This study characterizes the gut microbiota in BD and is the first to evaluate microbial changes following quetiapine monotherapy. Gut microbiota‐based biomarkers may be helpful in BD diagnosis and predicting treatment outcome, which need further validations.