Altered gut microbiome drives heightened pain sensitivity in a murine model of metastatic triple-negative breast cancer.

The microbiota residing in the gut environment is essential for host homeostasis. Increasing evidence suggests that microbial perturbation (dysbiosis) regulates cancer initiation and progression at local and distant sites. Here, we have identified microbial dysbiosis with the depletion of commensal bacteria as a host-intrinsic factor associated with metastatic dissemination to the bone. Using a mouse model of triple-negative mammary cancer, we demonstrate that a pre-established disruption of microbial homeostasis using an antibiotic cocktail increases tumor growth, enhanced circulating tumor cells, and subsequent dissemination to the bone. We found that the presence of pathogenic bacteria and loss of commensal bacteria in an antibiotic-induced gut environment is associated with sustained inflammation. Increased secretion of G-CSF and MMP-9 in intestinal tissues, followed by increased neutrophil infiltration and severe systemic inflammation in tumor-bearing mice, indicates the direct consequence of a dysbiotic microbiome. Increased neutrophil infiltration to the bone metastatic niche facilitates extravasation and transendothelial migration of tumor cells. It provides a novel, pre-established, and favorable environment to form an immunosuppressive pre-metastatic niche. The presence of tumor cells in immunosuppressive metastatic tumor niche disrupts the balance between osteoblasts and osteoclasts, promotes osteoclast differentiation, and remodels the bone structure. Excessive bone resorption by osteoclasts causes bone degradation and ultimately causes extreme pain in a bone metastatic mouse model. In clinical settings, bone metastasis is associated with intractable severe pain that severely compromises the quality of life in these patients.

LightNet: a novel lightweight convolutional network for brain tumor segmentation in healthcare.

Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the up-sampling stage, a light channel attention module is added to achieve richer multi-scale and spatial semantic feature information extraction of brain tumor. The performance of the network is evaluated in the Multimodal Brain Tumor Segmentation Challenge (BraTS 2015) dataset, and results are presented here alongside other high-performing CNNs. Results show comparable accuracy with other methods but with increased efficiency, segmentation performance, and reduced redundancy and computational complexity. The result is a high-performing network with a balance between efficiency and accuracy, allowing, for example, better energy performance on mobile devices.

Opioid-induced microbial dysbiosis disrupts irinotecan (CPT-11) metabolism and increases gastrointestinal toxicity in a murine model.

BACKGROUND AND PURPOSE: Opioids are commonly used for the management of cancer-associated pain and chemotherapy-induced diarrhoea. The chemotherapeutic irinotecan (CPT-11) causes severe gastrointestinal (GI) toxicity due to deconjugation of inactive metabolite SN-38 glucuronide (SN-38G) by bacterial ß-glucuronidases to the active 7-ethyl-10-hydroxycamptothecin (SN-38). Opioids are known to cause gut microbial dysbiosis, this study evaluated whether CPT-11 anti-tumour efficacy and GI toxicity are exacerbated by opioid co-administration. EXPERIMENTAL APPROACH: Eight-week-old C57BL/6 male mice were co-administration with CPT-11 ± opioid. 16S rRNA sequencing was used for gut microbiome analysis. LC-MS analyses of plasma and intestinal extracts were performed to investigate the pharmacokinetic profile of CPT-11. Histological analysis and quantitative real-time polymerase chain reaction were used to determine the severity of intestinal tissue damage. Human liver microsome In vitro assay was performed to confirm the effects of opioids on CPT-11 metabolism. KEY RESULTS: Gut microbiome analysis showed that morphine treatment induced enrichment of ß-glucuronidase-producing bacteria in the intestines of CPT-11-treated mice, resulting in SN-38 accumulation and exacerbation of GI toxicity in the small intestine. Oral administration of both antibiotics and glucuronidase inhibitor protected mice against GI toxicity induced with CPT-11 and morphine co-administration, implicating a microbiome-dependent mechanism. Additionally, morphine and loperamide decreased the plasma concentration of SN-38 and compromised CPT-11 anti-tumour efficacy, this seemed to be microbiome independent. CONCLUSION AND IMPLICATIONS: Gut microbiota play a significant role in opioid and chemotherapeutic agent drug-drug interactions. Inhibition of gut microbial glucuronidase may also prevent adverse GI effects of CPT-11 in patients on opioids.

HIV-Positive Patients on Antiretroviral Therapy Have an Altered Mucosal Intestinal but Not Oral Microbiome.

This study characterized compositional and functional shifts in the intestinal and oral microbiome in HIV-positive patients on antiretroviral therapy compared to HIV-negative individuals. Seventy-nine specimens were collected from 5 HIV-positive and 12 control subjects from five locations (colon brush, colon wash, terminal ileum [TI] brush, TI wash, and saliva) during colonoscopy and at patient visits. Microbiome composition was characterized using 16S rRNA sequencing, and microbiome function was predicted using bioinformatics tools (PICRUSt and BugBase). Our analysis indicated that the ß-diversity of all intestinal samples (colon brush, colon wash, TI brush, and TI wash) from patients with HIV was significantly different from patients without HIV. Specifically, bacteria from genera Prevotella, Fusobacterium, and Megasphaera were more abundant in samples from HIV-positive patients. On the other hand, bacteria from genera Ruminococcus, Blautia, and Clostridium were more abundant in samples from HIV-negative patients. Additionally, HIV-positive patients had higher abundances of biofilm-forming and pathogenic bacteria. Furthermore, pathways related to translation and nucleotide metabolism were elevated in HIV-positive patients, whereas pathways related to lipid and carbohydrate metabolism were positively correlated with samples from HIV-negative patients. Our analyses further showed variations in microbiome composition in HIV-positive and negative patients by sampling site. Samples from colon wash, colon brush, and TI wash were significant between groups, while samples from TI brush and saliva were not significant. Taken together, here, we report altered intestinal microbiome composition and predicted function in patients with HIV compared to uninfected patients, though we found no changes in the oral microbiome. IMPORTANCE Over 37 million people worldwide are living with HIV. Although the availability of antiretroviral therapy has significantly reduced the number of AIDS-related deaths, individuals living with HIV are at increased risk for opportunistic infections. We now know that HIV interacts with the trillions of bacteria, fungi, and viruses in the human body termed the microbiome. Only a limited number of previous studies have compared variations in the oral and gastrointestinal microbiome with HIV infection. Here, we detail how the oral and gastrointestinal microbiome changes with HIV infection, having used 5 different sampling sites to gain a more comprehensive view of these changes by location. Our results show site-specific changes in the intestinal microbiome associated with HIV infection. Additionally, we show that while there were significant changes in the intestinal microbiome, there were no significant changes in the oral microbiome.

Identification of novel early pancreatic cancer biomarkers KIF5B and SFRP2 from "first contact" interactions in the tumor microenvironment.

BACKGROUND: Pancreatic cancer is one of the most difficult cancers to detect early and most patients die from complications arising due to distant organ metastases. The lack of bona fide early biomarkers is one of the primary reasons for late diagnosis of pancreatic cancer. It is a multifactorial disease and warrants a novel approach to identify early biomarkers. METHODS: In order to characterize the proteome, Extracellular vesicles (EVs) isolated from different in vitro conditions mimicking tumor-microenvironment interactions between pancreatic cancer epithelial and stromal cells were analyzed using high throughput mass spectrometry. The biological activity of the secreted EVome was analyzed by investigating changes in distant organ metastases and associated early changes in the microbiome. Candidate biomarkers (KIF5B, SFRP2, LOXL2, and MMP3) were selected and validated on a mouse-human hybrid Tissue Microarray (TMA) that was specifically generated for this study. Additionally, a human TMA was used to analyze the expression of KIF5B and SFRP2 in progressive stages of pancreatic cancer. RESULTS: The EVome of co-cultured epithelial and stromal cells is different from individual cells with distinct protein compositions. EVs secreted from stromal and cancer cells cultures could not induce significant changes in Pre-Metastatic Niche (PMN) modulation, which was assessed by changes in the distant organ metastases. However, they did induce significant changes in the early microbiome, as indicated by differences in α and ß-diversities. KIF5B and SFRP2 show promise for early detection and investigation in progressive pancreatic cancer. These markers are expressed in all stages of pancreatic cancer such as low grade PanINs, advanced cancer, and in liver and soft tissue metastases. CONCLUSIONS: Proteomic characterization of EVs derived from mimicking conditions of epithelial and stromal cells in the tumor-microenvironment resulted in the identification of several proteins, some for the first time in EVs. These secreted EVs cannot induce changes in distant organ metastases in in vivo models of EV education, but modulate changes in the early murine microbiome. Among all the proteins that were analyzed (MMP3, KIF5B, SFRP2, and LOXL2), KIF5B and SFRP2 show promise as bona fide early pancreatic cancer biomarkers expressed in progressive stages of pancreatic cancer.