Gastric Cancer: The Microbiome Beyond Helicobacter pylori.

Gastric cancer remains an important global health burden. Helicobacter pylori is the major etiological factor in gastric cancer, infecting the stomach of almost half of the population worldwide. Recent progress in microbiome research offered a new perspective on the complexity of the microbial communities of the stomach. Still, the role of the microbiome of the stomach beyond H. pylori in gastric carcinogenesis is not well understood and requires deeper investigation. The gastric bacterial communities of gastric cancer patients are distinct from those of patients without cancer, but the microbial alterations that occur along the process of gastric carcinogenesis, and the mechanisms through which microorganisms influence cancer progression still need to be clarified. Except for Epstein-Barr virus, the potential significance of the virome and of the mycobiome in gastric cancer have received less attention. This chapter updates the current knowledge regarding the gastric microbiome, including bacteria, viruses, and fungi, within the context of H. pylori-mediated carcinogenesis. It also reviews the possible roles of the local gastric microbiota, as well as the microbial communities of the oral and gut ecosystems, as biomarkers for gastric cancer detection. Finally, it discusses future perspectives and acknowledges limitations in the area of microbiome research in the gastric cancer setting, to which further research efforts should be directed. These will be fundamental not only to increase our current understanding of host-microbial interactions but also to facilitate translation of the findings into innovative preventive, diagnostic, and therapeutic strategies to decrease the global burden of gastric cancer.

E-cadherin variants associated with oral facial clefts trigger aberrant cell motility in a REG1A-dependent manner.

BACKGROUND: Germline mutations of E-cadherin contribute to hereditary diffuse gastric cancer (HDGC) and congenital malformations, such as oral facial clefts (OFC). However, the molecular mechanisms through which E-cadherin loss-of-function triggers distinct clinical outcomes remain unknown. We postulate that E-cadherin-mediated disorders result from abnormal interactions with the extracellular matrix and consequent aberrant intracellular signalling, affecting the coordination of cell migration. METHODS: Herein, we developed in vivo and in vitro models of E-cadherin mutants associated with either OFC or HDGC. Using a Drosophila approach, we addressed the impact of the different variants in cell morphology and migration ability. By combining gap closure migration assays and time-lapse microscopy, we further investigated the migration pattern of cells expressing OFC or HDGC variants. The adhesion profile of the variants was evaluated using high-throughput ECM arrays, whereas RNA sequencing technology was explored for identification of genes involved in aberrant cell motility. RESULTS: We have demonstrated that cells expressing OFC variants exhibit an excessive motility performance and irregular leading edges, which prevent the coordinated movement of the epithelial monolayer. Importantly, we found that OFC variants promote cell adhesion to a wider variety of extracellular matrices than HDGC variants, suggesting higher plasticity in response to different microenvironments. We unveiled a distinct transcriptomic profile in the OFC setting and pinpointed REG1A as a putative regulator of this outcome. Consistent with this, specific RNAi-mediated inhibition of REG1A shifted the migration pattern of OFC expressing cells, leading to slower wound closure with coordinated leading edges. CONCLUSIONS: We provide evidence that E-cadherin variants associated with OFC activate aberrant signalling pathways that support dynamic rearrangements of cells towards improved adaptability to the microenvironment. This proficiency results in abnormal tissue shaping and movement, possibly underlying the development of orofacial malformations.

A metatranscriptomics strategy for efficient characterization of the microbiome in human tissues with low microbial biomass.

The high background of host RNA poses a major challenge to metatranscriptome analysis of human samples. Hence, metatranscriptomics has been mainly applied to microbe-rich samples, while its application in human tissues with low ratio of microbial to host cells has yet to be explored. Since there is no computational workflow specifically designed for the taxonomic and functional analysis of this type of samples, we propose an effective metatranscriptomics strategy to accurately characterize the microbiome in human tissues with a low ratio of microbial to host content. We experimentally generated synthetic samples with well-characterized bacterial and host cell compositions, and mimicking human samples with high and low microbial loads. These synthetic samples were used for optimizing and establishing the workflow in a controlled setting. Our results show that the integration of the taxonomic analysis of optimized Kraken 2/Bracken with the functional analysis of HUMAnN 3 in samples with low microbial content, enables the accurate identification of a large number of microbial species with a low false-positive rate, while improving the detection of microbial functions. The effectiveness of our metatranscriptomics workflow was demonstrated in synthetic samples, simulated datasets, and most importantly, human gastric tissue specimens, thus providing a proof of concept for its applicability on mucosal tissues of the gastrointestinal tract. The use of an accurate and reliable metatranscriptomics approach for human tissues with low microbial content will expand our understanding of the functional activity of the mucosal microbiome, uncovering critical interactions between the microbiome and the host in health and disease.

A review of machine learning methods for cancer characterization from microbiome data.

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

Whole blood transfusion in small animals: indications and effects

Transfusion therapy is a major resource that can improve the patient's capability to overcome the underlying disease. However, the effects of whole blood infusion, and how they affect the patient's outcome, are not yet clear. For this study, a protocol was developed in order to monitor a group of 15 animals (9 dogs, 6 cats) that received a total of 19 transfusions; 3 animals received more than one transfusion each. The most common indications for blood transfusion included acute blood loss (47 percent), coagulopathy (33 percent) and other anaemias (20 percent). The mean pre-transfusion packed cell volume (PCV) of animals with acute blood loss (18 percent) was higher than in the group of coagulopathy (15 percent) or other anaemias (15 percent). The survival rates at 6 days after transfusion were greater in the coagulopathy (80.0 percent) and other anaemias (66.7 percent) than in the group of acute blood loss (42.9 percent). After transfusion, pulse rate ( p <0.01) and platelet count ( p <0.05) decreased significantly, and there was a significant increase in body temperature of the animals that suffered from hypothermia before the transfusion ( p <0.05). Overall survival was predictable based upon posttransfusion body temperature, observed PCV change, the difference between the obtained and the calculated PCV, and administered transfusion volume ( p <0.05).

A terapia transfusional é um importante recurso que pode auxiliar na recuperação do doente face à patologia primária. No entanto, não estão totalmente esclarecidos os efeitos de uma transfusão sanguínea, e que consequências podem ter no desfecho clínico. Para este estudo, foi desenvolvido um protocolo de forma a monitorizar 15 animais (9 cães e 6 gatos) que receberam um total de 19 transfusões; 3 animais receberam mais do que uma unidade de sangue. As indicações mais comuns para realização da transfusão foram hemorragia aguda (47 por cento), coagulopatia (33 por cento) e outras anemias (20 por cento). O hematócrito pré-transfusional médio dos animais com hemorragia aguda (18 por cento) foi superior ao dos animais com coagulopatia (15 por cento) ou outras anemias (15 por cento). A taxa de sobrevivência ao sexto dia pós-transfusão foi maior no grupo da coagulopatia (80,0 por cento) e de outras anemias (66,7 por cento), do que no grupo da hemorragia aguda (42,9 por cento). Após a transfusão, a frequência de pulso ( p <0,01) e a contagem de plaquetas ( p <0,05) diminuíram significativamente, e houve um aumento significativo da temperatura corporal nos animais com hipotermia antes da transfusão ( p <0,05). A sobrevivência foi predizível com base na temperatura pós-transfusão, a variação do Ht, a diferença entre o Ht real e o Ht esperado, e o volume de transfusão aplicado ( p <0,05).