Comprehensive assessment of mRNA isoform detection methods for long-read sequencing data.

The advancement of Long-Read Sequencing (LRS) techniques has significantly increased the length of sequencing to several kilobases, thereby facilitating the identification of alternative splicing events and isoform expressions. Recently, numerous computational tools for isoform detection using long-read sequencing data have been developed. Nevertheless, there remains a deficiency in comparative studies that systemically evaluate the performance of these tools, which are implemented with different algorithms, under various simulations that encompass potential influencing factors. In this study, we conducted a benchmark analysis of thirteen methods implemented in nine tools capable of identifying isoform structures from long-read RNA-seq data. We evaluated their performances using simulated data, which represented diverse sequencing platforms generated by an in-house simulator, RNA sequins (sequencing spike-ins) data, as well as experimental data. Our findings demonstrate IsoQuant as a highly effective tool for isoform detection with LRS, with Bambu and StringTie2 also exhibiting strong performance. These results offer valuable guidance for future research on alternative splicing analysis and the ongoing improvement of tools for isoform detection using LRS data.

Trajectories of glycaemic traits exhibit sex-specific associations with hepatic iron and fat content: Results from the KORA-MRI study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) represents a major disease burden in the population. While the bidirectional association between NAFLD and diabetes is established, little is known about the association of hepatic iron content and glycaemia. Moreover, analyses of sex-specific effects and of dynamic changes in glycaemia are scarce. METHODS: We investigated 7-year sex-specific trajectories of glycaemia and related traits (HbA1c, fasting glucose, fasting insulin, HOMA-IR, 2-h glucose and cross-sectional 2-h insulin) in a sample from a population-based cohort (N = 365; 41.1% female). Hepatic iron and fat content were assessed by 3T-Magnetic Resonance Imaging (MRI). Two-step multi-level models adjusted for glucose-lowering medication and confounders were applied. RESULTS: In women and men, markers of glucose metabolism correlated with hepatic iron and fat content. Deterioration of glycaemia was associated with increased hepatic iron content in men (normoglycaemia to prediabetes: beta = 2.21 s-1 , 95% CI [0.47, 3.95]). Additionally, deterioration of glycaemia (e.g. prediabetes to diabetes: 1.27 log(%), [0.84, 1.70]) and trajectories of glucose, insulin and HOMA-IR were significantly associated with hepatic fat content in men. Similarly, deterioration of glycaemia as well as trajectories of glucose, insulin and HOMA-IR was significantly associated with increased hepatic fat content in women (e.g. trajectory of fasting insulin: 0.63 log(%), [0.36, 0.90]). CONCLUSIONS: Unfavourable 7-year trajectories of markers of glucose metabolism are associated with increased hepatic fat content, particularly in women, whereas the association with hepatic iron content was less clear. Monitoring changes of glycaemia in the sub-diabetic range might enable early identification of hepatic iron overload and steatosis.

Analysis of multiple chronic disease characteristics in middle-aged and elderly South Koreans by exercise habits based on association rules mining algorithm.

BACKGROUND: The term, "multiple chronic diseases" (MCD), describes a patient with two or more chronic conditions simultaneously at the same time. Compared with general chronic diseases, it is linked to poorer health outcomes, more difficult clinical management, and higher medical expenses. Several existing MCD guidelines support a healthy lifestyle including regular physical activities but do not include specific exercise therapy recommendations. This study aimed to understand the prevalence and model of MCD in middle-aged and elderly South Koreans by comparing MCD characteristics with exercise habits, to provide a theoretical basis for the implementation of exercise therapy in these patients. METHODS: The data of 8477 participants aged > 45 years from the "2020 Korean Health Panel Survey" were used to analyze the current status of MCD in the middle-aged and elderly. The Chi-square test for categorical variables and the t-test for continuous variables. the used software was IBM SPSS Statistics 26.0 and IBM SPSS Modeler 18.0. RESULTS: In this study, the morbidity rate of MCD was 39.1%. Those with MCD were more likely to be female (p < 0.001), seniors over 65 years of age (p < 0.001), with low education level, no regular exercise behavior (p < 0.01). Chronic renal failure (93.9%), depression (90.4%), and cerebrovascular disease (89.6%) were the top three diseases identified in patients with MCD. A total of 37 association rules were identified for the group of individuals who did not engage in regular exercise. This equated to 61% more than that of the regular exercise group, who showed only 23 association rules. In the extra association rules, cardiovascular diseases (150%), spondylosis (143%), and diabetes (125%) are the three chronic diseases with the highest frequency increase. CONCLUSIONS: Association rule analysis is effective in studying the relationship between various chronic diseases in patients with MCD. It also effectively helps with the identification of chronic diseases that are more sensitive to regular exercise behaviors. The findings from this study may be used to formulate more appropriate and scientific exercise therapy for patients with MCD.

Specific metabolites drive the deterministic assembly of diseased rhizosphere microbiome through weakening microbial degradation of autotoxin.

BACKGROUND: Process and function that underlie the assembly of a rhizosphere microbial community may be strongly linked to the maintenance of plant health. However, their assembly processes and functional changes in the deterioration of soilborne disease remain unclear. Here, we investigated features of rhizosphere microbiomes related to Fusarium wilt disease and assessed their assembly by comparison pair of diseased/healthy sequencing data. The untargeted metabolomics was employed to explore potential community assembly drivers, and shotgun metagenome sequencing was used to reveal the mechanisms of metabolite-mediated process after soil conditioning. RESULTS: Results showed the deterministic assembly process associated with diseased rhizosphere microbiomes, and this process was significantly correlated to five metabolites (tocopherol acetate, citrulline, galactitol, octadecylglycerol, and behenic acid). Application of the metabolites resulted in a deterministic assembly of microbiome with the high morbidity of watermelon. Furthermore, metabolite conditioning was found to weaken the function of autotoxin degradation undertaken by specific bacterial group (Bradyrhizobium, Streptomyces, Variovorax, Pseudomonas, and Sphingomonas) while promoting the metabolism of small-molecule sugars and acids initiated from another bacterial group (Anaeromyxobacter, Bdellovibrio, Conexibacter, Flavobacterium, and Gemmatimonas). Video Abstract CONCLUSION: These findings strongly suggest that shifts in a metabolite-mediated microbial community assembly process underpin the deterministic establishment of soilborne Fusarium wilt disease and reveal avenues for future research focusing on ameliorating crop loss due to this pathogen.

Krüppel-like factor 5 rewires NANOG regulatory network to activate human naive pluripotency specific LTR7Ys and promote naive pluripotency.

Endogenous retroviruses (ERVs) have been reported to participate in pre-implantation development of mammalian embryos. In early human embryogenesis, different ERV sub-families are activated in a highly stage-specific manner. How the specificity of ERV activation is achieved remains largely unknown. Here, we demonstrate the mechanism of how LTR7Ys, the human morula-blastocyst-specific HERVH long terminal repeats, are activated by the naive pluripotency transcription network. We find that KLF5 interacts with and rewires NANOG to bind and regulate LTR7Ys; in contrast, the primed-specific LTR7s are preferentially bound by NANOG in the absence of KLF5. The specific activation of LTR7Ys by KLF5 and NANOG in pluripotent stem cells contributes to human-specific naive pluripotency regulation. KLF5-LTR7Y axis also promotes the expression of trophectoderm genes and contributes to the expanded cell potential toward extra-embryonic lineage. Our study suggests that HERVs are activated by cell-state-specific transcription machinery and promote stage-specific transcription network and cell potency.

TAHDNet: Time-aware hierarchical dependency network for medication recommendation.

Medication recommendation is a hot topic in the research of applying neural networks to the healthcare area. Although extensive progressions have been made, current researches still face the following challenges: (i). Existing methods are poor at efficiently capturing and leveraging local and global dependency information from patient visit records. (ii). Current time-aware models based on irregularly interval medical records tend to ignore periodic variability in patient conditions, which limits the representational learning capability of these models. Therefore, we propose a Dynamic Time-aware Hierarchical Dependency Network (TAHDNet) for the medication recommendation task to address these challenges. Firstly, we use a Transformer-based model to learn the global information of the whole patient record through a self-supervised pre-training process. Secondly, a 1D-CNN model is used to learn the local dependencies on visitation level. Thirdly, we propose a dynamic time-aware module with a fused temporal decay function to assign different weights among different time intervals dynamically through a key-value attention mechanism. Experimental results on real-world datasets demonstrate the effectiveness of the model proposed in this paper.