Tag: Medication

Researchers from Rutgers University have shown that even limited exposure to modern medical care is associated with rapid changes in gut microbial diversity among remote Indigenous communities in the Amazon. The research, published in Cell Reports, studied the effects on Indigenous people in Venezuela where subsistence lifestyles have remained largely unchanged until the introduction of a World Health Organization–supported medical program for the treatment of the parasitic infectious disease onchocerciasis, also known as river blindness. After receiving medical care, the researchers discovered that gut microbial communities in the people began to shift toward patterns more commonly seen in industrialized populations after only a few medical visits and included measurable declines in microbial diversity.

“The study gives us a better idea of how sensitive human gut microbes are,” said senior author Maria G. Dominguez-Bello, PhD, a professor of microbiome and health at Rutgers. “It opens the door for future research on how we can restore our microbiota after using medicines like antibiotics, which can deplete organisms in our gut.”

Prior research has established that urbanization-related changes in diet, lifestyle, and environment can alter the gut microbiome, but those factors are often intertwined. In this research, the unique setting and population allowed the Rutgers team to isolate the effects of repeated medical exposure in populations with minimal prior contact with modern healthcare.

“Many factors contribute to reduced microbial diversity associated with Westernization, complicating efforts to identify early drivers of microbiome change,” the researchers noted, adding that the Amazonian villages provided a setting where “low-exposure villages show higher baseline gut microbiota diversity than the medium-exposure village, and microbiota diversity declines over time in association with repeated exposure, particularly in children.”

The study followed 335 participants across multiple villages, collecting fecal samples and body-site swabs before and during repeated medical visits between October 2015 and February 2016. The villages were categorized by prior exposure to outsiders and medicine, allowing comparisons between low-exposure communities and a medium-exposure village that had a longer history of modern medical services. Researchers collected samples from the gut, mouth, nose, and skin in conjunction with the WHO’s quarterly visits that delivered antiparasitic treatments and basic care.

“This study leverages a rare longitudinal dataset from remote Amazonian Indigenous communities to examine how the human microbiome shifts during the earliest stages of contact with external institutions, prior to major dietary or lifestyle urbanization,” the researchers wrote.

In the gut microbiome, data from the collected samples showed a decline in taxa commonly associated with fiber metabolism and an increased abundance of bacterial groups more commonly seen in industrialized populations. Functional gene profiles also shifted, with increased representation of genes linked to simple carbohydrate metabolism and antimicrobial resistance, and reduced representation of genes involved in fiber fermentation and other metabolic processes.

The study also found that microbial changes were most pronounced in children, pointing toward a heightened sensitivity of developing microbiomes to repeated medical exposure. In addition to gut changes, oral, nasal, and skin microbiomes also shifted: oral microbial diversity declined; nasal diversity increased after initial visits; and skin communities showed reductions in diversity and shifts in composition.

Previous research cited by the authors has linked industrialized lifestyles with reduced microbial diversity, altered microbial networks, and increased prevalence of genes associated with antimicrobial resistance. However, the current study provides a unique glimpse at the effects of the earliest stages of treatment by modern medicine before shifts in diet or infrastructure occur.

This new data highlights the need to find the balance between essential medical interventions and preservation of microbial diversity. While treatments such as those for river blindness remain critical for reducing infectious disease burden, the researchers suggest that repeated exposure to medical care may coincide with microbial restructuring that reduces diversity and alters functional capacity.

“Understanding and navigating this balance is essential not only for microbiome science but also for ethical, culturally informed approaches that respect both biological and social dimensions of wellbeing,” the researchers noted.

Future research will examine ways to protect microbial diversity during necessary medical interventions and expanding longitudinal studies that assess resilience and recovery of the microbiome after repeated exposure to medicines.

The post Early Medical Care Linked to Rapid Gut Microbiome Shifts in Amazonian Indigenous Communities appeared first on Inside Precision Medicine.

RNA splicing, in which different coding RNA, or exons, are joined together after noncoding regions, or introns, are removed, allows for a large array of RNA transcript isoforms with distinct sequences, and functions in tissue- and cell-type-specific patterns. Conversely, transcript isoform alterations can sensitively reflect dynamic changes in cellular states. Aberrant splicing is closely associated with major diseases, such as cancer.  

In a new study published in Nature Computational Science titled, “HELIX: a scalable model for predicting context-dependent regulation of RNA splicing and isoform usage,” researchers from the Chinese Academy of Sciences have developed an AI-driven framework that enables highly accurate prediction of RNA splicing and isoform usage by integrating genomic sequence features with tissue-specific RNA binding protein (RBP) expression profiles. The work offers valuable insights for splicing regulatory patterns, pathogenic variant interpretation, and precision medicine research. 

Isoform usage is jointly regulated by multiple layers of control, including regulatory elements, such as splicing enhancers and silencers on exons and introns, and tissue microenvironments. Scientists have been challenged to accurately characterize and predict RNA splicing and isoform usage across tissues, cell types, and disease states. 

The study’s AI framework, Hierarchical Explainable LSTM for Isoform eXpression (HELIX), overcomes the limitations of conventional approaches via a two-layer deep-learning architecture. 

First, the framework integrates DNA sequence information with the expression profiles of 1,499 RBPs. Long short-term memory (LSTM) networks are then employed to effectively capture the complex dependencies and competitive relationships among multiple splice sites. 

This design enables precise, reliable prediction of RNA splicing and transcript isoform usage. The model was trained and optimized on large-scale short- and long-read RNA-seq datasets covering 30 distinct human tissues, allowing accurate quantification of complex transcript structures and isoform usage. Results show that HELIX substantially outperforms existing mainstream methods in both splicing strength prediction and overall isoform usage prediction. 

In disease-related studies, HELIX deciphered aberrant RNA splicing and transcript isoform alterations. Notably, the researchers identified widespread splicing dysregulation and abnormal isoform usage in tumor cells using large colorectal cancer cohorts. 

The results reveal strong correlations among such alterations and genomic mutations, RBP dysregulation, and patient clinical profiles. Results support that splicing abnormalities can serve as key molecular signatures for tumor progression and guiding patient stratification. 

The team also developed scHELIX, a single-cell RNA sequencing extension of HELIX. scHELIX supports high-resolution profiling of transcript isoform usage across different cell types and tumor subpopulations, which offer a refined view of intratumoral heterogeneity. 

The findings reveal distinct RNA splicing and isoform usage patterns among tumor subclones, providing new clues for tumor evolution research and potential therapeutic target discovery. 

The post HELIX AI Model Accurately Predicts RNA Splicing, Unlocks Precision Medicine appeared first on GEN – Genetic Engineering and Biotechnology News.