Impact of nutritional support routes on mortality in acute pancreatitis: A network meta-analysis of randomized controlled trials.

BACKGROUND: Nutritional administration in acute pancreatitis (AP) management has sparked widespread discussion, yet contradictory mortality results across meta-analyses necessitate clarification. The optimal nutritional route in AP remains uncertain. Therefore, this study aimed to compare mortality among nutritional administration routes in patients with AP using consistency model. METHODS: This study searched four major databases for relevant randomized controlled trials (RCTs). Two authors independently extracted and checked data and quality. Network meta-analysis was conducted for estimating risk ratios (RRs) with 95% confidence interval (CI) based on random-effects model. Subgroup analyses accounted for AP severity and nutrition support initiation. RESULTS: A meticulous search yielded 1185 references, with 30 records meeting inclusion criteria from 27 RCTs (n = 1594). Pooled analyses showed the mortality risk reduction associated with nasogastric (NG) (RR = 0.34; 95%CI: 0.16-0.73) and nasojejunal (NJ) feeding (RR = 0.46; 95%CI: 0.25-0.84) in comparison to nil per os. Similarly, NG (RR = 0.45; 95%CI: 0.24-0.83) and NJ (RR = 0.60; 95%CI: 0.40-0.90) feeding also showed lower mortality risk than total parenteral nutrition. Subgroup analyses, stratified by severity, supported these findings. Notably, the timing of nutritional support initiation emerged as a significant factor, with NJ feeding demonstrating notable mortality reduction within 24 and 48 h, particularly in severe cases. CONCLUSION: For severe AP, both NG and NJ feeding appear optimal, with variations in initiation timings. NG feeding does not appear to merit recommendation within the initial 24 h, whereas NJ feeding is advisable within the corresponding timeframe following admission. These findings offer valuable insights for optimizing nutritional interventions in AP.

Insulin-like growth factor 2 mRNA-binding protein 3 enhanced melanoma migration through regulation of AKT1 and RELA expression.

IMP-3 expression is a poor prognostic factor of melanomas and it promotes melanoma cell migration and invasion by a pathway modulating HMGA2 mRNA expression. We tried to identify other putative targets of IMP-3. We identified putative IMP-3-binding RNAs, including AKT1, MAPK3, RB1 and RELA, by RNA immunoprecipitation coupled with next-generation sequencing. IMP-3 overexpression increased AKT and RELA levels in MeWo cells. siRNAs against AKT1 and RELA inhibited MeWo/Full-length IMP-3 cell migration. IMP-3 knockdown of A2058 cells decreased AKT1 and RELA expression and lowered migration ability. Co-transfection of A2058 cells with AKT1- or RELA-expressing plasmids with IMP-3 siRNA restored the inhibitory effects of IMP-3 knockdown on migration. HMGA2 did not influence AKT1 and RELA expression in melanoma cells. Human melanoma samples with high IMP-3 levels also showed high HMGA2, AKT1 and RELA expression. Our results show that IMP-3 enhances melanoma cell migration through the regulation of the AKT1 and RELA axis.

Global Transcriptome Analysis Reveals Distinct Phases of the Endothelial Response to TNF.

The vascular endothelium acts as a dynamic interface between blood and tissue. TNF-α, a major regulator of inflammation, induces endothelial cell (EC) transcriptional changes, the overall response dynamics of which have not been fully elucidated. In the present study, we conducted an extended time-course analysis of the human EC response to TNF, from 30 min to 72 h. We identified regulated genes and used weighted gene network correlation analysis to decipher coexpression profiles, uncovering two distinct temporal phases: an acute response (between 1 and 4 h) and a later phase (between 12 and 24 h). Sex-based subset analysis revealed that the response was comparable between female and male cells. Several previously uncharacterized genes were strongly regulated during the acute phase, whereas the majority in the later phase were IFN-stimulated genes. A lack of IFN transcription indicated that this IFN-stimulated gene expression was independent of de novo IFN production. We also observed two groups of genes whose transcription was inhibited by TNF: those that resolved toward baseline levels and those that did not. Our study provides insights into the global dynamics of the EC transcriptional response to TNF, highlighting distinct gene expression patterns during the acute and later phases. Data for all coding and noncoding genes is provided on the Web site (http://www.endothelial-response.org/). These findings may be useful in understanding the role of ECs in inflammation and in developing TNF signaling-targeted therapies.

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data.

MOTIVATION: Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. RESULTS: We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data. AVAILABILITY AND IMPLEMENTATION: The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.

Heterogeneity in the gene regulatory landscape of leiomyosarcoma.

Characterizing inter-tumor heterogeneity is crucial for selecting suitable cancer therapy, as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. While cancer subtypes are often characterized by differences in gene expression, the mechanisms driving these differences are generally unknown. We set out to model the regulatory mechanisms driving sarcoma heterogeneity based on patient-specific, genome-wide gene regulatory networks. We developed a new computational framework, PORCUPINE, which combines knowledge on biological pathways with permutation-based network analysis to identify pathways that exhibit significant regulatory heterogeneity across a patient population. We applied PORCUPINE to patient-specific leiomyosarcoma networks modeled on data from The Cancer Genome Atlas and validated our results in an independent dataset from the German Cancer Research Center. PORCUPINE identified 37 heterogeneously regulated pathways, including pathways representing potential targets for treatment of subgroups of leiomyosarcoma patients, such as FGFR and CTLA4 inhibitory signaling. We validated the detected regulatory heterogeneity through analysis of networks and chromatin states in leiomyosarcoma cell lines. We showed that the heterogeneity identified with PORCUPINE is not associated with methylation profiles or clinical features, thereby suggesting an independent mechanism of patient heterogeneity driven by the complex landscape of gene regulatory interactions.

Taiwan Controlled Substances Database.

New psychoactive substances (NPS) have increasingly been illegally synthesized and used around the world in recent years. Due to the large volume and the variety of NPS, most do not have sufficient information about their addictive potential and harmful effects to human subjects. This makes it difficult to evaluate these potential substances of abuse. This study aims to build a database based on Taiwan's controlled substances, to provide quick structural and pharmacological feedback. Taiwan Controlled Substances Database (TCSD) includes the collection of controlled substances, relevant experimental and structural information, as well as computational features such as molecular fingerprints and descriptors. Two types of structural search were added: substructure search and topological fingerprint similarity search. A web framework was used to enhance accessibility and usability (https://cs2search.cmdm.tw).

Development of a photothermal bead-based nucleic acid amplification test (pbbNAAT) technique for a high-performance loop-mediated isothermal amplification (LAMP)-based point-of-care test (POCT).

We have developed a novel molecular diagnostic platform (photothermal bead-based nucleic acid amplification test; pbbNAAT) that greatly improves the low sensitivity of direct loop-mediated isothermal amplification (LAMP) and allows for specific detection of LAMP amplicons in complex samples. The pbbNAAT integrates specific ligand-functionalized polypyrrole-coated iron oxide particles (PPy@IOs) capable of photothermal conversion and single-molecule magnetic capture of target analytes, the released nucleic acid, and LAMP-amplified products under external light energy control and magnetic manipulations. This allows for sample pretreatment, pbbLAMP amplification, and subsequent amplicon detection with bead-based ELISA in a one-stop microreactor without loss. In addition, photonic heating with PPy@IOs and external light control provide instant and uniform heating for thermolysis and pbbLAMP implementations. Moreover, it generates higher primer annealing stringency for LAMP primers in pbbLAMP; thus, it can detect pathogen-specific DNA accurately and promptly in pathogen-spiked complex materials. The sample pretreatment procedure of pbbNAAT can greatly reduce inhibitors originating from complex samples, which enables the maintenance of maximal enzyme activities for highly sensitive detection. More importantly, the pbbLAMP assay coupled with magnetic capture permits subsequent bead-based ELISA detection to determine true positive LAMP amplicons on PPy@IOs. The pbbNAAT platform has a high tolerance to inhibitors originating from complex samples, high analytical specificity, and limitation of detection (LoD) as low as 8 CFU/reaction to detect E. coli spiked in human whole blood, bovine milk, and can be completed in less than 1 h. Therefore, we believe that pbbNAAT can serve as a suitable direct LAMP platform for on-site POCTs.

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel deep learning method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

GenEpi: gene-based epistasis discovery using machine learning.

BACKGROUND: Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD). RESULTS: In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power. CONCLUSIONS: The results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future.

Author Correction: Effect of de novo transcriptome assembly on transcript quantification.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

lionessR: single sample network inference in R.

BACKGROUND: In biomedical research, network inference algorithms are typically used to infer complex association patterns between biological entities, such as between genes or proteins, using data from a population. This resulting aggregate network, in essence, averages over the networks of those individuals in the population. LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) is a method that can be used together with a network inference algorithm to extract networks for individual samples in a population. The method's key characteristic is that, by modeling networks for individual samples in a data set, it can capture network heterogeneity in a population. LIONESS was originally made available as a function within the PANDA (Passing Attributes between Networks for Data Assimilation) regulatory network reconstruction framework. However, the LIONESS algorithm is generalizable and can be used to model single sample networks based on a wide range of network inference algorithms. RESULTS: In this software article, we describe lionessR, an R implementation of LIONESS that can be applied to any network inference method in R that outputs a complete, weighted adjacency matrix. As an example, we provide a vignette of an application of lionessR to model single sample networks based on correlated gene expression in a bone cancer dataset. We show how the tool can be used to identify differential patterns of correlation between two groups of patients. CONCLUSIONS: We developed lionessR, an open source R package to model single sample networks. We show how lionessR can be used to inform us on potential precision medicine applications in cancer. The lionessR package is a user-friendly tool to perform such analyses. The package, which includes a vignette describing the application, is freely available at: https://github.com/kuijjerlab/lionessR and at: http://bioconductor.org/packages/lionessR .

Infection rate among nutritional therapies for acute pancreatitis: A systematic review with network meta-analysis of randomized controlled trials.

BACKGROUND: Infection in acute pancreatitis (AP) is associated with nutritional therapies including naso-gastric (NG), naso-jejunal (NJ), and total parenteral nutrition (TPN). To examine infections among NG, NJ, TPN, and no nutritional support (NNS) in treating patients with AP. METHODS: The investigators completed comprehensive search in the Cochrane library, EMBASE, PubMed, Web of Science, and ClinicalTrials.gov without restriction on language and publication date before January 21, 2019. They also searched the reference lists of relevant studies for randomized controlled trials (RCTs) comparing NG, NJ, TPN, and NNS among patients with AP. Quantitative synthesis was conducted in a contrast-based network meta-analysis. To clarify effects, a network meta-analysis was conducted to calculate the surface under the cumulative ranking curve (SUCRA). Beside of overall infections, the event rates of infected pancreatic necrosis, bacteremia, line infection, pneumonia, urinary tract infection, and other types of infections were measured. RESULTS: The network meta-analysis of 16 RCTs showed that NJ had significantly lower overall infection rates compared with TPN (risk ratio: 0.59; 95% confidence interval: 0.38, 0.90); and NG had a larger effect size and higher rank probability compared with NJ, TPN, and NNS (mean rank = 1.7; SUCRA = 75.8). TPN was the least preferred (mean rank = 3.2; SUCRA = 26.6). CONCLUSIONS: NG and NJ may be preferred therapies for treating patients with AP. Clinicians may consider NG as a first-line treatment for patients with AP (including severe AP) and even in patients receiving prophylactic antibiotics. In addition, we found that NNS should be avoided when treating patients with severe AP.

Effect of de novo transcriptome assembly on transcript quantification.

Correct quantification of transcript expression is essential to understand the functional elements in different physiological conditions. For the organisms without the reference transcriptome, de novo transcriptome assembly must be carried out prior to quantification. However, a large number of erroneous contigs produced by the assemblers might result in unreliable estimation. In this regard, this study investigates how assembly quality affects the performance of quantification based on de novo transcriptome assembly. We examined the over-extended and incomplete contigs, and demonstrated that assembly completeness has a strong impact on the estimation of contig abundance. Then we investigated the behavior of the quantifiers with respect to sequence ambiguity which might be originally presented in the transcriptome or accidentally produced by assemblers. The results suggested that the quantifiers often over-estimate the expression of family-collapse contigs and under-estimate the expression of duplicated contigs. For organisms without reference transcriptome, it remains challenging to detect the inaccurate estimation on family-collapse contigs. On the contrary, we observed that the situation of under-estimation on duplicated contigs can be warned through analyzing the read proportion of estimated abundance (RPEA) of contigs in the connected component inferenced by the quantifiers. In addition, we suggest that the estimated quantification results on the connected component level have better accuracy over sequence level quantification. The analytic results conducted in this study provides valuable insights for future development of transcriptome assembly and quantification.

Early molecular events associated with nitrogen deficiency in rice seedling roots.

Nitrogen (N) deficiency is one of the most common problems in rice. The symptoms of N deficiency are well documented, but the underlying molecular mechanisms are largely unknown in rice. Here, we studied the early molecular events associated with N starvation (-N, 1 h), focusing on amino acid analysis and identification of -N-regulated genes in rice roots. Interestingly, levels of glutamine rapidly decreased within 15 min of -N treatment, indicating that part of the N-deficient signals could be mediated by glutamine. Transcriptome analysis revealed that genes involved in metabolism, plant hormone signal transduction (e.g. abscisic acid, auxin, and jasmonate), transporter activity, and oxidative stress responses were rapidly regulated by -N. Some of the -N-regulated genes encode transcription factors, protein kinases and protein phosphatases, which may be involved in the regulation of early -N responses in rice roots. Previously, we used similar approaches to identify glutamine-, glutamate-, and ammonium nitrate-responsive genes. Comparisons of the genes induced by different forms of N with the -N-regulated genes identified here have provided a catalog of potential N regulatory genes for further dissection of the N signaling pathwys in rice.

Whole-genome de novo sequencing reveals unique genes that contributed to the adaptive evolution of the Mikado pheasant.

Background: The Mikado pheasant (Syrmaticus mikado) is a nearly endangered species indigenous to high-altitude regions of Taiwan. This pheasant provides an opportunity to investigate evolutionary processes following geographic isolation. Currently, the genetic background and adaptive evolution of the Mikado pheasant remain unclear. Results: We present the draft genome of the Mikado pheasant, which consists of 1.04 Gb of DNA and 15,972 annotated protein-coding genes. The Mikado pheasant displays expansion and positive selection of genes related to features that contribute to its adaptive evolution, such as energy metabolism, oxygen transport, hemoglobin binding, radiation response, immune response, and DNA repair. To investigate the molecular evolution of the major histocompatibility complex (MHC) across several avian species, 39 putative genes spanning 227 kb on a contiguous region were annotated and manually curated. The MHC loci of the pheasant revealed a high level of synteny, several rapidly evolving genes, and inverse regions compared to the same loci in the chicken. The complete mitochondrial genome was also sequenced, assembled, and compared against four long-tailed pheasants. The results from molecular clock analysis suggest that ancestors of the Mikado pheasant migrated from the north to Taiwan about 3.47 million years ago. Conclusions: This study provides a valuable genomic resource for the Mikado pheasant, insights into its adaptation to high altitude, and the evolutionary history of the genus Syrmaticus, which could potentially be useful for future studies that investigate molecular evolution, genomics, ecology, and immunogenetics.

Identification of early ammonium nitrate-responsive genes in rice roots.

Ammonium has long been used as the predominant form of nitrogen source for paddy rice (Oryza sativa). Recently, increasing evidence suggests that nitrate also plays an important role for nitrogen acquisition in the rhizosphere of waterlogged paddy rice. Ammonium and nitrate have a synergistic effect on promoting rice growth. However, the molecular responses induced by simultaneous treatment with ammonium and nitrate have been less studied in rice. Here, we performed transcriptome analysis to identify genes that are rapidly regulated by ammonium nitrate (1.43 mM, 30 min) in rice roots. The combination of ammonium and nitrate preferentially induced the expression of nitrate-responsive genes. Gene ontology enrichment analysis revealed that the early ammonium nitrate-responsive genes were enriched in "regulation of transcription, DNA-dependent" and "protein amino acid phosphorylation" indicating that some of the genes identified in this study may play an important role in nitrogen sensing and signaling. Several defense/stress-responsive genes, including some encoding transcription factors and mitogen-activated protein kinase kinase kinases, were also rapidly induced by ammonium nitrate. These results suggest that nitrogen metabolism, signaling, and defense/stress responses are interconnected. Some of the genes identified here may be involved in the interaction of nitrogen signaling and defense/stress-response pathways in plants.