MTM: a multi-task learning framework to predict individualized tissue gene expression profiles.

MOTIVATION: Transcriptional profiles of diverse tissues provide significant insights in both fundamental and translational researches, while transcriptome information is not always available for tissues that require invasive biopsies. Alternatively, predicting tissue expression profiles from more accessible "surrogate" samples, especially blood transcriptome, has become a promising strategy when invasive procedures are not practical. However, existing approaches ignore tissue-shared intrinsic relevance, inevitably limiting predictive performance. RESULTS: We propose a unified deep learning-based multi-task learning framework, multi-tissue transcriptome mapping (MTM), enabling the prediction of individualized expression profiles from any available tissue of an individual. By jointly leveraging individualized cross-tissue information from reference samples through multi-task learning, MTM achieves superior sample-level and gene-level performance on unseen individuals. With the high prediction accuracy and the ability to preserve individualized biological variations, MTM could facilitate both fundamental and clinical biomedical research. AVAILABILITY AND IMPLEMENTATION: MTM's code and documentation are available upon publication on GitHub (https://github.com/yangence/MTM).

Ecological risk assessment and identification of the distinct microbial groups in heavy metal-polluted river sediments.

To assess the health of river ecosystems, it is essential to quantify the ecological risk of heavy metals in river sediments and the structure of microbial communities. As important tributaries of the Tuo River in the upper reaches of the Yangtze River, the Mianyuan River and the Shiting River, are closely related to the economic development and human daily life in the region. This study assessed the ecological risks of heavy-metal-polluted river sediments, the heavy-metal-driven bacterial communities were revealed, and the relationships between the ecological risks and the identical bacterial communities were discussed. The Cd content was significantly greater than the environmental background value, leading to a serious pollution and very high ecological risk at the confluence of the two rivers and the upper reaches of the Mianyuan River. Microbial community analysis showed that Rhodobacter, Nocardioides, Sphingomonas, and Pseudarthrobacter were the dominant bacterial genera in the sediments of the Shiting River. However, the dominant bacterial genera in the Mianyuan River were Kouleothrix, Dechloromonas, Gaiella, Pedomicrobium, and Hyphomicrobium. Mantel test results showed (r = 0.5977, P = 0.005) that the Cd, As, Zn, Pb, Cr, and Cu were important factors that influenced differences in the distribution of sediment bacterial communities Mianyuan and Shiting rivers. A correlation heatmap showed that heavy metals were negatively correlated for most bacterial communities, but some bacterial communities were tolerant and showed a positive correlation. Overall, the microbial structure of the river sediments showed a diverse spatial distribution due to the influence of heavy metals. The results will improve the understanding of rivers contaminated by heavy metals and provide theoretical support for conservation and in situ ecological restoration of river ecosystems.

Distribution of sediment microbial communities and their relationship with surrounding environmental factors in a typical rural river, Southwest China.

With rapid urbanization and industrialization, rural rivers in China are facing deterioration in water quality and ecosystem health. Microorganisms living in river sediments are involved in biogeochemical processes, mineralization, and degradation of pollutants. Understanding bacterial community distribution in rural rivers could help evaluate the response of river ecosystems to environmental pollution and understand the river self-purification mechanism. In this study, the relationship between characteristics of sediment microbial communities and the surrounding environmental factors in a typical rural river was analyzed using 16S rRNA gene sequencing technology. The results showed that the dominant bacterial groups in the river sediment were Proteobacteria, Actinobacteria, Chloroflexi, Acidobacteria, Bacteroidetes, and Firmicutes, accounting for 83.61% of the total microbial load. Different areas have different sources of pollution which give rise to specific dominant bacteria. The upstream part of the river flows through an agricultural cultivation area where the dominant bacteria were norank_f_Gemmatimonadaceae, Haliangium, and Pseudolabrys, possessing obvious nitrogen- and phosphorus-metabolizing activities. The midstream section flows through an urban area where the dominant bacteria were Marmoricola, Nocardioides, Gaiella, Sphingomonas, norank_f_67-14, Subgroup_10, Agromyces, and Lysobacter, with strong metabolizing activity for toxic pollutants. The dominant bacteria in the downstream part were Clostridium_sensu_stricto_1, norank_f__Bacteroidetes_vadinHA17, Candidatus_Competibacter, and Methylocystis. Redundancy analysis and correlation heatmap analysis showed that environmental factors: ammonia nitrogen (NH4+-N) and total nitrogen (TN) in the sediment, and pH, temperature, TN, electrical conductivity (EC), and total dissolved solids (TDS) in the water, significantly affected the bacterial community in the sediment. The PICRUSt2 functional prediction analysis identified that the main function of bacteria in the sediment was metabolism (77.3%), specifically carbohydrate, amino acid, and energy metabolism. These activities are important for degrading organic matter and removing pollutants from the sediments. The study revealed the influence of organic pollutants derived from human activities on the bacterial community composition in the river sediments. It gave a new insight into the relationship between environmental factors and bacterial community distribution in rural watershed ecosystems, providing a theoretical basis for self-purification and bioremediation of rural rivers.