Do Extracellular RNAs Provide Insight into Uveal Melanoma Biology?

Uveal melanoma (UM) is the most common primary intraocular malignant tumor in adults, showing a high mortality due to metastasis. Although it is considered a rare disease, a growing number of papers have reported altered levels of RNAs (i.e., coding and non-coding RNAs) in cancerous tissues and biological fluids from UM patients. The presence of circulating RNAs, whose dysregulation is associated with UM, paved the way to the possibility of exploiting it for diagnostic and prognostic purposes. However, the biological meaning and the origin of such RNAs in blood and ocular fluids of UM patients remain unexplored. In this review, we report the state of the art of circulating RNAs in UM and debate whether the amount and types of RNAs measured in bodily fluids mirror the RNA alterations from source cancer cells. Based on literature data, extracellular RNAs in UM patients do not represent, with rare exceptions, a snapshot of RNA dysregulations occurring in cancerous tissues, but rather the complex and heterogeneous outcome of a systemic dysfunction, including immune system activity, that modifies the mechanisms of RNA delivery from several cell types.

Drug Design: Where We Are and Future Prospects

Medicinal chemistry is facing new challenges in approaching precision medicine. Several powerful new tools or improvements of already used tools are now available to medicinal chemists to help in the process of drug discovery, from a hit molecule to a clinically used drug. Among the new tools, the possibility of considering folding intermediates or the catalytic process of a protein as a target for discovering new hits has emerged. In addition, machine learning is a new valuable approach helping medicinal chemists to discover new hits. Other abilities, ranging from the better understanding of the time evolution of biochemical processes to the comprehension of the biological meaning of the data originated from genetic analyses, are on their way to progress further in the drug discovery field toward improved patient care. In this sense, the new approaches to the delivery of drugs targeted to the central nervous system, together with the advancements in understanding the metabolic pathways for a growing number of drugs and relating them to the genetic characteristics of patients, constitute important progress in the field.

Development of a bioinformatics platform for analysis of quantitative transcriptomics and proteomics data: the OMnalysis

Background In the past decade, RNA sequencing and mass spectrometry based quantitative approaches are being used commonly to identify the differentially expressed biomarkers in different biological conditions. Data generated from these approaches come in different sizes (e.g., count matrix, normalized list of differentially expressed biomarkers, etc.) and shapes (e.g., sequences, spectral data, etc.). The list of differentially expressed biomarkers is used for functional interpretation and retrieve biological meaning, however, it requires moderate computational skills. Thus, researchers with no programming expertise find difficulty in data interpretation. Several bioinformatics tools are available to analyze such data; however, they are less flexible for performing the multiple steps of visualization and functional interpretation. Implementation We developed an easy-to-use Shiny based web application (named as OMnalysis) that provides users with a single platform to analyze and visualize the differentially expressed data. The OMnalysis accepts the data in tabular form from edgeR, DESeq2, MaxQuant Perseus, R packages, and other similar software, which typically contains the list of differentially expressed genes or proteins, log of the fold change, log of the count per million, the P value, q-value, etc. The key features of the OMnalysis are multiple image type visualization and their dimension customization options, seven multiple hypothesis testing correction methods to get more significant gene ontology, network topology-based pathway analysis, and multiple databases support (KEGG, Reactome, PANTHER, biocarta, NCI-Nature Pathway Interaction Database PharmGKB and STRINGdb) for extensive pathway enrichment analysis. OMnalysis also fetches the literature information from PubMed to provide supportive evidence to the biomarkers identified in the analysis. In a nutshell, we present the OMnalysis as a well-organized user interface, supported by peer-reviewed R packages with updated databases for quick interpretation of the differential transcriptomics and proteomics data to biological meaning. Availability The OMnalysis codes are entirely written in R language and freely available at https://github.com/Punit201016/OMnalysis. OMnalysis can also be accessed from - http://lbmi.uvlf.sk/omnalysis.html. OMnalysis is hosted on a Shiny server at https://omnalysis.shinyapps.io/OMnalysis/. The minimum system requirements are: 4 gigabytes of RAM, i3 processor (or equivalent). It is compatible with any operating system (windows, Linux or Mac). The OMnalysis is heavily tested on Chrome web browsers; thus, Chrome is the preferred browser. OMnalysis works on Firefox and Safari.

Are There Limits in Explainability of Prognostic Biomarkers? Scrutinizing Biological Utility of Established Signatures

Prognostic biomarkers can have an important role in the clinical practice because they allow stratification of patients in terms of predicting the outcome of a disorder. Obstacles for developing such markers include lack of robustness when using different data sets and limited concordance among similar signatures. In this paper, we highlight a new problem that relates to the biological meaning of already established prognostic gene expression signatures. Specifically, it is commonly assumed that prognostic markers provide sensible biological information and molecular explanations about the underlying disorder. However, recent studies on prognostic biomarkers investigating 80 established signatures of breast and prostate cancer demonstrated that this is not the case. We will show that this surprising result is related to the distinction between causal models and predictive models and the obfuscating usage of these models in the biomedical literature. Furthermore, we suggest a falsification procedure for studies aiming to establish a prognostic signature to safeguard against false expectations with respect to biological utility.

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.

Nitrogen Use Efficiency Definitions of Today and Tomorrow

Crop production has a large impact on the nitrogen (N) cycle, with consequences to climate, environment, and public health. Designing better N management will require indicators that accurately reflect the complexities of N cycling and provide biological meaning. Nitrogen use efficiency (NUE) is an established metric used to benchmark N management. There are numerous approaches to calculate NUE, but it is difficult to find an authoritative resource that collates the various NUE indices and systematically identifies their assets and shortcomings. Furthermore, there is reason to question the usefulness of many traditional NUE formulations, and to consider factors to improve the conceptualization of NUE for future use. As a resource for agricultural researchers and students, here we present a comprehensive list of NUE indices and discuss their functions, strengths, and limitations. We also suggest several factors—which are currently ignored in traditional NUE indices—that will improve the conceptualization of NUE, such as: accounting for a wider range of soil N forms, considering how plants mediate their response to the soil N status, including the below-ground/root N pools, capturing the synchrony between available N and plant N demand, blending agronomic performance with ecosystem functioning, and affirming the biological meaning of NUE.

Stability of May’s Host–Parasitoid model with variable stocking upon parasitoids

Using the Kolmogorov–Arnold–Mozer (KAM) theory, we investigate the stability of May’s host–parasitoid model’s solutions with proportional stocking upon the parasitoid population. We show the existence of the extinction, boundary, and interior equilibrium points. When the host population’s intrinsic growth rate and the releasement coefficient are less than one, both populations are extinct. There are an infinite number of boundary equilibrium points, which are nonhyperbolic and stable. Under certain conditions, there appear 1:1 nonisolated resonance fixed points for which we thoroughly described dynamics. Regarding the interior equilibrium point, we use the KAM theory to prove its stability. We give a biological meaning of obtained results. Using the software package Mathematica, we produce numerical simulations to support our findings.

Selenium Transport Mechanism via Selenoprotein P—Its Physiological Role and Related Diseases

Selenoprotein P (SELENOP) is selenium (Se)-containing protein in plasma, which is primarily produced in the liver. The “P” in SELENOP originated from the presence in plasma. SELENOP contains selenocysteine, a cysteine analog containing Se instead of sulfur. SELENOP is a multi-functional protein to reduce phospholipid hydroperoxides and to deliver Se from the liver to other tissues, such as those of the brain and testis, playing a pivotal role in Se metabolism and antioxidative defense. Decrease in SELENOP causes various dysfunctions related to Se deficiency and oxidative stress, while excessive SELENOP causes insulin resistance. This review focuses on the Se transport system of SELENOP, particularly its molecular mechanism and physiological role in Se metabolism. Furthermore, the chemical form of Se and its biological meaning is discussed.