Helicobacter pylori (H. pylori), discovered in 1982, is a microaerophilic, spiral-shaped gram-negative bacterium that is able to colonize the human stomach. Nearly half of the world's population is infected by this pathogen. Its ability to induce gastritis, peptic ulcers, gastric cancer and mucosa-associated lymphoid tissue lymphoma has been confirmed. The susceptibility of an individual to these clinical outcomes is multifactorial and depends on H. pylori virulence, environmental factors, the genetic susceptibility of the host and the reactivity of the host immune system. Despite the host immune response, H. pylori infection can be difficult to eradicate. H. pylori is categorized as a group I carcinogen since this bacterium is responsible for the highest rate of cancer-related deaths worldwide. Early detection of cancer can be lifesaving. The 5-year survival rate for gastric cancer patients diagnosed in the early stages is nearly 90%. Gastric cancer is asymptomatic in the early stages but always progresses over time and begins to cause symptoms when untreated. In 97% of stomach cancer cases, cancer cells metastasize to other organs. H. pylori infection is responsible for nearly 60% of the intestinal-type gastric cancer cases but also influences the development of diffuse gastric cancer. The host genetic susceptibility depends on polymorphisms of genes involved in H. pylori-related inflammation and the cytokine response of gastric epithelial and immune cells. H. pylori strains differ in their ability to induce a deleterious inflammatory response. H. pylori-driven cytokines accelerate the inflammatory response and promote malignancy. Chronic H. pylori infection induces genetic instability in gastric epithelial cells and affects the DNA damage repair systems. Therefore, H. pylori infection should always be considered a pro-cancerous factor.
Commensal microbiota plays a critical role in the maintenance of human health. Microbes influence energy metabolism and nutrient absorption and help defend the host organism against pathogens. The composition of the gut microbiota is delicately balanced, and any alterations may lead to proinflammatory immune responses and initiation of disease processes, including cancer. Experimental evidence indicates that the human intestinal microbiota can influence tumour development and progression in the gastrointestinal tract by damaging DNA, activation of oncogenic signaling pathways, production of tumour-promoting metabolites, and suppression of the anti-tumour immune response. The aim of this article was to outline differences in human microbiota between healthy subjects and patients with gastrointestinal malignancies such as esophageal, stomach, liver, biliary tract, pancreas and colon inflammations, and cancers. A better understanding of microbiota changes in various gastrointestinal malignancies will enable a greater insight into the relationship between human microbiota composition and cancer development.
Komensalna mikroflora jelit odgrywa kluczową rolę w utrzymaniu homeostazy w ludzkim organizmie. Drobnoustroje wpływają między innymi na przemianę energii i wchłanianie składników odżywczych, regulują pracę układu immunologicznego oraz pomagają chronić organizm gospodarza przed patogennymi mikroorganizmami. Skład mikroflory jelitowej występuje w łatwej do zaburzenia równowadze, a wszelkie jej zmiany wywołane dietą, stresem, otyłością, chorobami układu pokarmowego czy przyjmowaniem leków, mogą prowadzić do prozapalnych odpowiedzi immunologicznych i zapoczątkowania procesów chorobowych, w tym nowotworowych. Utrzymanie homeostazy mikroflory jelit jest zatem niezwykle istotne dla zdrowia człowieka. W celu jej przywrócenia najczęściej stosowane jest przyjmowanie preparatów zawierających odpowiednie kultury bakterii tj. probiotyków. W związku z faktem, iż jogurty stanowią źródło bakterii probiotycznych, ich regularne spożycie stanowić może mocny punkt w profilaktyce różnego rodzaju chorób w tym cywilizacyjnych jak i nowotworowych. Artykuł ten stanowi przegląd literatury dotyczącej zastosowaniabakterii jogurtowych w profilaktyce chorób nowotworowych. Zagadnienia poruszone w artykule dotyczą charakterystyki bakterii jogurtowych, dobroczynnego wpływu probiotyków na zdrowie człowieka, antynowotworowych właściwości bakterii jogurtowych oraz ich metabolitów tj.: immunoregulacji, zapobiegania infekcjom bakteryjnym, utrzymania połączeń komórkowych w jelicie i przeciwnowotworowej aktywności metabolitów bakteryjnych.
Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited, which indicates the need for further research on alignment-free methods. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that the deep learning model manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies that contextualize metagenomic data are a promising direction to deeply understand the microbiome.
Human gut microbiome is increasingly recognized as a crucial factor in human health and disease. While most studies have focused on cross-sectional data, there is growing interest in longitudinal studies that capture the dynamics of the microbiome over time. Investigating the temporal dynamics of the microbiome, including individual bacterial species and clusters, is essential for comprehending its functionality and impact on health. This knowledge has implications for targeted therapeutic strategies, such as personalized diets and probiotic therapy. Here, by adopting a rigorous statistical approach, we aim to shed light on the temporal changes in the gut microbiome and unravel its intricate behavior over time. We leveraged four long and dense time series of the gut microbiome in generally healthy individuals examining how its composition evolves as a community and how individual bacterial species behave over time. We also explore whether specific clusters of bacteria exhibit similar fluctuations, which could provide insights into potential functional relationships and interactions within the human gut microbiome. Our study reveals that despite its high volatility human gut microbiome is stable in time and can be predicted based solely on its previous states. Employing a statistical approach, we characterize the unique temporal behavior of individual bacterial species. Furthermore, we identify distinct longitudinal regimes in which bacteria exhibit specific patterns of behavior. Additionally, through cluster analysis, we identify groups of bacteria that exhibit coordinated fluctuations over time. These findings contribute to our understanding of the dynamic nature of the gut microbiome and its potential implications for human health.
Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that our model (ArdiMiPE) manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies such as ArdiMiPE that contextualize metagenomic data are a promising direction to deeply understand the microbiome.
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