Demantbrock4334
The mechanisms that coordinate cellular gene expression are highly complex and intricately interconnected. Thus, it is necessary to move beyond a fully reductionist approach to understanding genetic information flow and begin focusing on the networked connections between genes that organize cellular function. Continued advancements in computational hardware, coupled with the development of gene correlation network algorithms, provide the capacity to study networked interactions between genes rather than their isolated functions. For example, gene coexpression networks are used to construct gene relationship networks using linear metrics such as Spearman or Pearson correlation. Recently, there have been tools designed to deepen these analyses by differentiating between intrinsic vs extrinsic noise within gene expression values, identifying different modules based on tissue phenotype, and capturing potential nonlinear relationships. In this report, we introduce an algorithm with a novel application of image-based segmentation modalities utilizing blob detection techniques applied for detecting bigenic edges in a gene expression matrix. We applied this algorithm called EdgeCrafting to a bulk RNA-sequencing gene expression matrix comprised of a healthy kidney and cancerous kidney data. We then compared EdgeCrafting against 4 other RNA expression analysis techniques Weighted Gene Correlation Network Analysis, Knowledge Independent Network Construction, NetExtractor, and Differential gene expression analysis.Assessing central carbon metabolism in plants can be challenging due to the dynamic range in pool sizes, with low levels of important phosphorylated sugars relative to more abundant sugars and organic acids. Here, we report a sensitive liquid chromatography-mass spectrometry (LC-MS) method for analyzing central metabolites on a hybrid column, where both anion-exchange and hydrophilic interaction chromatography (HILIC) ligands are embedded in the stationary phase. The LC method was developed for enhanced selectivity of 27 central metabolites in a single run with sensitivity at femtomole levels observed for most phosphorylated sugars. The method resolved phosphorylated hexose, pentose, and triose isomers that are otherwise challenging. Compared to a standard HILIC approach, these metabolites had improved peak areas using our approach due to ion-enhancement or low ion-suppression in the biological sample matrix. The approach was applied to investigate metabolism in high lipid-producing tobacco leaves that exhibited increased levels of the acetyl-CoA, a precursor for oil biosynthesis. The application of the method to isotopologue detection and quantification was considered through evaluating 13C-labeled seeds from Camelina sativa. The method provides a means to analyze intermediates more comprehensively in central metabolism of plant tissues.The combination of chemotherapy and immune therapies still promises to synergize for prolonged tumor control. However, the quest for optimal combinations tailored for tumor histology remains ongoing. A recent study provides evidence on the feasibility of the trabectedin/durvalumab combination and reports on interesting preliminary efficacy. See related article by Toulmonde et al., p. 1765.Recent years have seen an increase in the number of structures available, not only for new proteins but also for the same protein crystallized with different molecules and proteins. While protein design software have proven to be successful in designing and modifying proteins, they can also be overly sensitive to small conformational differences between structures of the same protein. To cope with this, we introduce here pyFoldX, a python library that allows the integrative analysis of structures of the same protein using FoldX, an established forcefield and modeling software. The library offers new functionalities for handling different structures of the same protein, an improved molecular parametrization module, and an easy integration with the data analysis ecosystem of the python programming language.
pyFoldX rely on the FoldX software for energy calculations and modelling, which can be downloaded upon registration in http//foldxsuite.crg.eu/ and its licence is free of charge for academics. The pyFoldX library is open-source. Full details on installation, tutorials covering the library functionality, and the scripts used to generate the data and figures presented in this paper are available at https//github.com/leandroradusky/pyFoldX.
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.Acoustic emission analysis is promising to investigate the physiological events leading to drought-induced injury and mortality. However, their nature and source are not fully understood, making this technique difficult to use as a direct measure of the loss of xylem hydraulic conductance. Acoustic emissions were recorded during severe dehydration in lavender plants (Lavandula angustifolia) and compared with the dynamics of embolism development and cell damage. The timing and characteristics of acoustic signals from two independent recording systems were compared by principal component analysis (PCA). Changes in water potential, branch diameter, loss of hydraulic conductance, and cellular damage were also measured to quantify drought-induced damages. Two distinct phases of acoustic emissions were observed during dehydration the first one associated with a rapid loss of diameter and a significant increase in loss of xylem conductance (90%), and the second with slower changes in diameter and a significant increase in cellular damage. Based on PCA, a developed algorithm discriminated hydraulic-related acoustic signals from other sources, proposing a reconstruction of hydraulic vulnerability curves. Cellular damage preceded by hydraulic failure seems to lead to a lack of recovery. The second acoustic phase would allow detection of plant mortality.The ubiquitin-proteasome system is associated with various phenomena including learning and memory. In this study, we report that E3 ubiquitin ligase homologs and proteasome function are involved in taste avoidance learning, a type of associative learning between starvation and salt concentrations, in Caenorhabditis elegans. Pharmacological inhibition of proteasome function using bortezomib causes severe defects in taste avoidance learning. Among 9 HECT-type ubiquitin ligase genes, loss-of-function mutations of 6 ubiquitin ligase genes cause significant abnormalities in taste avoidance learning. Double mutations of those genes cause lethality or enhanced defects in taste avoidance learning, suggesting that the HECT-type ubiquitin ligases act in multiple pathways in the processes of learning. Furthermore, mutations of the ubiquitin ligase genes cause additive effects on taste avoidance learning defects of the insulin-like signaling mutants. Our findings unveil the consequences of aberrant functions of the proteasome and ubiquitin systems in learning behavior of Caenorhabditis elegans.
Protein-protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions have been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are known to suffer various limitations such as exaggerated false positives and negatives rates. The semantic similarity derived from the Gene Ontology (GO) annotation is regarded as one of the most powerful indicators for protein interactions. However, while computational approaches for prediction of PPIs have gained popularity in recent years, most methods fail to capture the specificity of GO terms.
We propose TransformerGO, a model that is capable of capturing the semantic similarity between gene ontology sets dynamically using an attention mechanism. We generate dense graph embeddings for GO terms using an algorithmic framework for learning continuous representations of nodes in networks called node2vec. TransformerGO learns deep semantic relations between annotated terms and can distinguish between negative and positive interactions with high accuracy. TransformerGO outperforms classic semantic similarity measures on gold standard PPI datasets and state-of-the-art machine learning-based approaches on large datasets from S. cerevisiae and H. sapiens. We show how the neural attention mechanism embedded in the transformer architecture detects relevant functional terms when predicting interactions.
https//github.com/Ieremie/TransformerGO.
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Inferring an accurate gene regulatory network (GRN) has long been a key goal in the field of systems biology. In order to do this, it is important to find a suitable balance between the maximum number of true positive and the minimum number of false positive interactions. Another key feature is that the inference method can handle the large size of modern experimental data, meaning the method needs to be both fast and accurate. The LSCO (Least Squares Cut-Off) method can fulfill both these criteria, however as it is based on least squares it is vulnerable to known issues of amplifying extreme values, small or large. In GRN this manifests itself with genes that are erroneously hyper-connected to a large fraction of all genes due to extremely low value fold changes.
We developed a GRN inference method called LSCON (Least Squares Cut-Off with Normalization) that tackles this problem. selleck chemicals LSCON extends the LSCO algorithm by regularization to avoid hyper-connected genes and thereby reduce false positives. The regularization employed is based on normalization, which removes effects of extreme values on the fit. We benchmarked LSCON and compared it to Genie3, LASSO, LSCO, and Ridge regression, in terms of accuracy, speed, and tendency to predict hyper-connected genes. The results show that LSCON achieves better or equal accuracy compared to LASSO, the best existing method, especially for data with extreme values. Thanks to the speed of least squares regression, LSCON does this an order of magnitude faster than LASSO.
Data https//bitbucket.org/sonnhammergrni/lscon; Code https//bitbucket.org/sonnhammergrni/genespider.
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Testing safety of Delta24-RGD (DNX-2401), an oncolytic adenovirus, locally delivered by convection enhanced delivery (CED) in tumor and surrounding brain of patients with recurrent glioblastoma.
Dose-escalation phase I study with 3+3 cohorts, dosing 107 to 1 × 1011 viral particles (vp) in 20 patients. Besides clinical parameters, adverse events, and radiologic findings, blood, cerebrospinal fluid (CSF), brain interstitial fluid, and excreta were sampled over time and analyzed for presence of immune response, viral replication, distribution, and shedding.
Of 20 enrolled patients, 19 received the oncolytic adenovirus Delta24-RGD, which was found to be safe and feasible. Four patients demonstrated tumor response on MRI, one with complete regression and still alive after 8 years. Most serious adverse events were attributed to increased intracranial pressure caused by either an inflammatory reaction responding to steroid treatment or viral meningitis being transient and self-limiting. Often viral DNA concentrations in CSF increased over time, peaking after 2 to 4 weeks and remaining up to 3 months.
