Correlation of gestational age and age at death in sudden infant death syndrome: another pointer to the role of critical developmental period?

BACKGROUND: Filiano and Kinney proposed a triple-risk model for the sudden infant death syndrome (SIDS) that involves the intersection of three risks: (1) a vulnerable infant, (2) a critical developmental period in homeostatic control, and (3) an exogenous stressor(s). The primary evidence for the role of a critical developmental period in SIDS etiology is the peak of cases around the third month of life. Independently, several studies pointed to correlation between gestational age and age at death in SIDS, but used that to assess the SIDS risk for preterm infants, ignoring further ramifications. METHODS: We did a detailed analysis of CDC data spanning over two decades (1983-2011). We focused not only on the correlation between two age variables (gestational and age at death), but also on the possibility of misdiagnosis. Also, we attempted to account for potential biases in the data induced by the ICD-9/ICD-190 transition or the "Back to Sleep" campaign. RESULTS: The peak of deaths in the third month of life, that was the main argument for the role of the critical development period, wasn't unique to SIDS. However, we confirmed an almost linear and negative correlation between gestational age and the week of death due to SIDS. This pattern (slope of correlation < 0 and significance of correlation p < 0.05) is characteristic of SIDS among all diseases analyzed in the study. CONCLUSIONS: We interpret the results as the evidence of the role of the critical development period in SIDS etiology. Possibly more attention in the future research should be put to theories that are based on homeostatic control.

Interactions of piRNAs with the mRNA of Candidate Genes in Esophageal Squamous Cell Carcinoma.

Recently, a database of human piRNAs (piwi-interacting RNAs) was created, which allows the study of the binding of many piRNAs to the mRNAs of genes involved in many diseases, including cancer. In the present work, we identified the piRNAs that can interact with candidate esophageal squamous cell carcinoma (ESCC) genes. The binding of 480 thousand piRNAs with the mRNAs of 66 candidate ESCC genes was studied. Bioinformatic studies found that piRNAs bind only to the mRNAs of nine candidate genes: AURKA, BMP7, GCOM1, ERCC1, MTHFR, SASH1, SIX4, SULT1A1, and TP53. It has been shown that piRNAs can bind to mRNA by overlapping nucleotide sequences in limited 3'UTR and 5'UTR regions called clusters of binding sites (BSs). The existence of clusters of piRNA BSs significantly reduces the proportion of the nucleotide sequences of these sites in the mRNA of target genes. Competition between piRNAs occurs for binding to the mRNA of target genes. Individual piRNAs and groups of piRNAs that have separate BSs and clusters of BSs in the mRNAs of two or more candidate genes have been identified in the mRNAs of these genes. This organization of piRNAs BSs indicates the interdependence of the expression of candidate genes through piRNAs. Significant differences in the ability of genes to interact with piRNAs prevent the side effects of piRNAs on genes with a lack of the ability to bind such piRNAs. Individual piRNAs and sets of piRNAs are proposed and recommended for the diagnosis and therapy of ESCC.

Endogenous piRNAs Can Interact with the Omicron Variant of the SARS-CoV-2 Genome.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the COVID-19 pandemic, can still infect populations in many countries around the globe. The Omicron strain is the most mutated variant of SARS-CoV-2. The high transmissibility of the strain and its ability to evade immunity necessitate a priority study of its properties in order to quickly create effective means of preventing its spread. The current research aimed to examine the in silico interaction between PIWI-interacting RNAs (piRNAs) and the SARS-CoV-2 genome (gRNA) to identify endogenous piRNAs and propose synthetic piRNAs with strong antiviral activity for drug development. This study used validated bioinformatic approaches regarding the interaction of more than eight million piRNAs with the SARS-CoV-2 genome. The piRNAs' binding sites (BSs) in the 5'UTR were located with overlapping nucleotide sequences termed clusters of BSs. Several BSs clusters have been found in the nsp3, nsp7, RNA-dependent RNA polymerase, endoRNAse, S surface glycoprotein, ORF7a, and nucleocapsid. Sixteen synthetic piRNAs that interact with gRNA have been proposed with free binding energy ranging from -170 kJ/mol to -175 kJ/mol, which can be used to create drugs that suppress the reproduction of SARS-CoV-2.

Antioxidant Activity and Photosynthesis Efficiency in Melissa officinalis Subjected to Heavy Metals Stress.

The aim of this study was to assess influence of cadmium and zinc treatments on antioxidant activity combined with the photosynthesis efficiency in a popular herb lemon balm (Melissa officinalis L.). Plants were grown under greenhouse conditions by the pot method. The Mn, Cu, Cd, and Zn contents in soil and plants were measured by HR-CS FAAS. The activity of net photosynthesis, stomatal conductance, transpiration rate, intercellular CO2, and index of chlorophyll in leaves were determined for all investigated species. Reduction of the net photosynthesis was observed for cultivations subjected to either Zn or Cd treatments. Phenolic contents were determined by the chemical Folin-Ciocalteu method, while enhanced voltammetric analysis was applied to assess the antioxidant properties of plant extracts. Both of these approaches yielded similar results. Herbal extracts had exceptional antioxidant capacities and were good scavengers of free radicals and reactive oxygen species. Structural similarity of cadmium and zinc facilitated their mutual structural exchange and prompted substantial expansion of phenolics under the mixed Zn and Cd treatments.

New Advances in Short Peptides: Looking Forward.

It is beyond doubt that short peptides hold significant promise in bio-medicine, as the most versatile molecules, both structurally and functionally [...].

Native Structure-Based Peptides as Potential Protein-Protein Interaction Inhibitors of SARS-CoV-2 Spike Protein and Human ACE2 Receptor.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is a positive-strand RNA virus that causes severe respiratory syndrome in humans, which is now referred to as coronavirus disease 2019 (COVID-19). Since December 2019, the new pathogen has rapidly spread globally, with over 65 million cases reported to the beginning of December 2020, including over 1.5 million deaths. Unfortunately, currently, there is no specific and effective treatment for COVID-19. As SARS-CoV-2 relies on its spike proteins (S) to bind to a host cell-surface receptor angiotensin-converting enzyme-2(ACE2), and this interaction is proved to be responsible for entering a virus into host cells, it makes an ideal target for antiviral drug development. In this work, we design three very short peptides based on the ACE2 sequence/structure fragments, which may effectively bind to the receptor-binding domain (RBD) of S protein and may, in turn, disrupt the important virus-host protein-protein interactions, blocking early steps of SARS-CoV-2 infection. Two of our peptides bind to virus protein with affinity in nanomolar range, and as very short peptides have great potential for drug development.

A Global Review on Short Peptides: Frontiers and Perspectives.

Peptides are fragments of proteins that carry out biological functions. They act as signaling entities via all domains of life and interfere with protein-protein interactions, which are indispensable in bio-processes. Short peptides include fundamental molecular information for a prelude to the symphony of life. They have aroused considerable interest due to their unique features and great promise in innovative bio-therapies. This work focusing on the current state-of-the-art short peptide-based therapeutical developments is the first global review written by researchers from all continents, as a celebration of 100 years of peptide therapeutics since the commencement of insulin therapy in the 1920s. Peptide "drugs" initially played only the role of hormone analogs to balance disorders. Nowadays, they achieve numerous biomedical tasks, can cross membranes, or reach intracellular targets. The role of peptides in bio-processes can hardly be mimicked by other chemical substances. The article is divided into independent sections, which are related to either the progress in short peptide-based theranostics or the problems posing challenge to bio-medicine. In particular, the SWOT analysis of short peptides, their relevance in therapies of diverse diseases, improvements in (bio)synthesis platforms, advanced nano-supramolecular technologies, aptamers, altered peptide ligands and in silico methodologies to overcome peptide limitations, modern smart bio-functional materials, vaccines, and drug/gene-targeted delivery systems are discussed.

Host microRNA analysis in cyprinid Herpesvirus-3 (CyHV-3) infected common carp.

BACKGROUND: The mechanism of latency and the ability of the cyprinid herpesvirus 3 (CyHV-3) to establish life-long infections in carp remains poorly understood. To explain the role of miRNAs in this process we applied a range of molecular tools including high-throughput sequencing of RNA libraries constructed from the blood samples of infected fish followed by bioinformatic and functional analyses which show that CyHV-3 profoundly influences the expression of host miRNAs in vivo. RESULTS: We demonstrated the changed expression of 27 miRNAs in the clinical phase and 5 in the latent phase of infection. We also identified 23 novel, not previously reported sequences, from which 8 showed altered expressions in control phase, 10 in clinical phase and 5 in latent phase of infection. CONCLUSIONS: The results of our analysis expand the knowledge of common carp microRNAs engaged during CyHV-3 infection and provide a useful basis for the further study of the mechanism of CyHV-3 induced pathology.

Development and evaluation of a deep learning model for protein-ligand binding affinity prediction.

Motivation: Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to 'learn' to extract features that are relevant for the task at hand. Results: We have developed a novel deep neural network estimating the binding affinity of ligand-receptor complexes. The complex is represented with a 3D grid, and the model utilizes a 3D convolution to produce a feature map of this representation, treating the atoms of both proteins and ligands in the same manner. Our network was tested on the CASF-2013 'scoring power' benchmark and Astex Diverse Set and outperformed classical scoring functions. Availability and implementation: The model, together with usage instructions and examples, is available as a git repository at http://gitlab.com/cheminfIBB/pafnucy. Supplementary information: Supplementary data are available at Bioinformatics online.

Tools4miRs - one place to gather all the tools for miRNA analysis.

UNLABELLED: MiRNAs are short, non-coding molecules that negatively regulate gene expression and thereby play several important roles in living organisms. Dozens of computational methods for miRNA-related research have been developed, which greatly differ in various aspects. The substantial availability of difficult-to-compare approaches makes it challenging for the user to select a proper tool and prompts the need for a solution that will collect and categorize all the methods. Here, we present tools4miRs, the first platform that gathers currently more than 160 methods for broadly defined miRNA analysis. The collected tools are classified into several general and more detailed categories in which the users can additionally filter the available methods according to their specific research needs, capabilities and preferences. Tools4miRs is also a web-based target prediction meta-server that incorporates user-designated target prediction methods into the analysis of user-provided data. AVAILABILITY AND IMPLEMENTATION: Tools4miRs is implemented in Python using Django and is freely available at tools4mirs.org. CONTACT: piotr@ibb.waw.pl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detection of Plant miRNAs Abundance in Human Breast Milk.

Breast milk is a natural food and important component of infant nutrition. Apart from the alimentary substances, breast milk contains many important bioactive compounds, including endogenous microRNA molecules (miRNAs). These regulatory molecules were identified in various mammalian biological fluids and were shown to be mostly packed in exosomes. Recently, it was revealed that plant food-derived miRNAs are stably present in human blood and regulate the expression of specific human genes. Since then, the scientific community has focused its efforts on contradicting or confirming this discovery. With the same intention, qRT-PCR experiments were performed to evaluate the presence of five plant food-derived miRNAs (miR166a, miR156a, miR157a, miR172a and miR168a) in breast milk (whole milk and exosomes) from healthy volunteers. In whole milk samples, all examined miRNAs were identified, while only two of these miRNAs were confirmed to be present in exosomes. The plant miRNA concentration in the samples ranged from 4 to 700 fM. Complementary bioinformatics analysis suggests that the evaluated plant miRNAs may potentially influence several crucial biological pathways in the infant organism.

DeCAF-Discrimination, Comparison, Alignment Tool for 2D PHarmacophores.

Comparison of small molecules is a common component of many cheminformatics workflows, including the design of new compounds and libraries as well as side-effect predictions and drug repurposing. Currently, large-scale comparison methods rely mostly on simple fingerprint representation of molecules, which take into account the structural similarities of compounds. Methods that utilize 3D information depend on multiple conformer generation steps, which are computationally expensive and can greatly influence their results. The aim of this study was to augment molecule representation with spatial and physicochemical properties while simultaneously avoiding conformer generation. To achieve this goal, we describe a molecule as an undirected graph in which the nodes correspond to atoms with pharmacophoric properties and the edges of the graph represent the distances between features. This approach combines the benefits of a conformation-free representation of a molecule with additional spatial information. We implemented our approach as an open-source Python module called DeCAF (Discrimination, Comparison, Alignment tool for 2D PHarmacophores), freely available at http://bitbucket.org/marta-sd/decaf. We show DeCAF's strengths and weaknesses with usage examples and thorough statistical evaluation. Additionally, we show that our method can be manually tweaked to further improve the results for specific tasks. The full dataset on which DeCAF was evaluated and all scripts used to calculate and analyze the results are also provided.

A Specialized Histone H1 Variant Is Required for Adaptive Responses to Complex Abiotic Stress and Related DNA Methylation in Arabidopsis.

Linker (H1) histones play critical roles in chromatin compaction in higher eukaryotes. They are also the most variable of the histones, with numerous nonallelic variants cooccurring in the same cell. Plants contain a distinct subclass of minor H1 variants that are induced by drought and abscisic acid and have been implicated in mediating adaptive responses to stress. However, how these variants facilitate adaptation remains poorly understood. Here, we show that the single Arabidopsis (Arabidopsis thaliana) stress-inducible variant H1.3 occurs in plants in two separate and most likely autonomous pools: a constitutive guard cell-specific pool and a facultative environmentally controlled pool localized in other tissues. Physiological and transcriptomic analyses of h1.3 null mutants demonstrate that H1.3 is required for both proper stomatal functioning under normal growth conditions and adaptive developmental responses to combined light and water deficiency. Using fluorescence recovery after photobleaching analysis, we show that H1.3 has superfast chromatin dynamics, and in contrast to the main Arabidopsis H1 variants H1.1 and H1.2, it has no stable bound fraction. The results of global occupancy studies demonstrate that, while H1.3 has the same overall binding properties as the main H1 variants, including predominant heterochromatin localization, it differs from them in its preferences for chromatin regions with epigenetic signatures of active and repressed transcription. We also show that H1.3 is required for a substantial part of DNA methylation associated with environmental stress, suggesting that the likely mechanism underlying H1.3 function may be the facilitation of chromatin accessibility by direct competition with the main H1 variants.

Dominance from the perspective of gene-gene and gene-chemical interactions.

In this study, we used genetic interaction (GI) and gene-chemical interaction (GCI) data to compare mutations with different dominance phenotypes. Our analysis focused primarily on Saccharomyces cerevisiae, where haploinsufficient genes (HI; genes with dominant loss-of-function mutations) were found to be participating in gene expression processes, namely, the translation and regulation of gene transcription. Non-ribosomal HI genes (mainly regulators of gene transcription) were found to have more GIs and GCIs than haplosufficient (HS) genes. Several properties seem to lead to the enrichment of interactions, most notably, the following: importance, pleiotropy, gene expression level and gene expression variation. Importantly, after these properties were appropriately considered in the analysis, the correlation between dominance and GI/GCI degrees was still observed. Strikingly, for the GCIs of heterozygous strains, haploinsufficiency was the only property significantly correlated with the number of GCIs. We found ribosomal HI genes to be depleted in GIs/GCIs. This finding can be explained by their high variation in gene expression under different genetic backgrounds and environmental conditions. We observed the same distributions of GIs among non-ribosomal HI, ribosomal HI and HS genes in three other species: Schizosaccharomyces pombe, Drosophila melanogaster and Homo sapiens. One potentially interesting exception was the lack of significant differences in the degree of GIs between non-ribosomal HI and HS genes in Schizosaccharomyces pombe.

Plant MicroRNAs-Novel Players in Natural Medicine?

MicroRNAs (miRNAs) represent a class of small non-coding RNAs that act as efficient gene expression regulators and thus play many important roles in living organisms. Due to their involvement in several known human pathological and pathogenic states, miRNA molecules have become an important issue in medicine and gained the attention of scientists from the pharmaceutical industry. In recent few years, a growing number of studies have provided evidence that miRNAs may be transferred from one species to another and regulate gene expression in the recipients' cells. The most intriguing results revealed that stable miRNAs derived from food plants may enter the mammals' circulatory system and, after reaching the target, inhibit the production of specific mammalian protein. Part of the scientific community has perceived this as an attractive hypothesis that may provide a foundation for novel therapeutic approaches. In turn, others are convinced about the "false positive" effect of performed experiments from which the mentioned results were achieved. In this article, we review the recent literature that provides evidence (from both fronts) of dietary, plant miRNA uptake and functionality in various consumers. Additionally, we discuss possible miRNA transport mechanisms from plant food sources to human cells.

The clinical response to vemurafenib in a patient with a rare BRAFV600DK601del mutation-positive melanoma.

BACKGROUND: Mutations in the activation segment of the v-raf murine sarcoma viral oncogene homolog B (BRAF) gene are present in approximately 50% of melanomas. The selective BRAF inhibitor vemurafenib has demonstrated significant clinical benefits in patients with melanomas harboring the most common mutations (V600E, V600K and V600R). However, the clinical activity of BRAF inhibitors in patients with rare mutations of codon 600 and the surrounding codons has not been documented. CASE PRESENTATION: We used the BRAF inhibitor vemurafenib to treat a patient presenting a rare p.V600_K601delinsD-mutated melanoma. An objective response was evidenced by two months of progression-free survival. By cloning and sequencing BRAF exon 15, we confirmed that a dual mutation was present on a single allele and thus resulted in a BRAFV(600DK601del) mutant protein. We also performed an in silico crystal structure analysis of the mutated protein, in order to characterize the nature of the putative interaction between vemurafenib and the mutant protein. CONCLUSION: This clinical experience suggests that (i) patients with BRAFV(600DK601del)-mutation-positive melanoma can be treated successfully with the oral BRAF inhibitor vemurafenib and (ii) molecular screening in this context should encompass rare and complex mutations.

DiSCuS: an open platform for (not only) virtual screening results management.

DiSCuS, a "Database System for Compound Selection", has been developed. The primary goal of DiSCuS is to aid researchers in the steps subsequent to generating high-throughput virtual screening (HTVS) results, such as selection of compounds for further study, purchase, or synthesis. To do so, DiSCuS provides (1) a storage facility for ligand-receptor complexes (generated with external programs), (2) a number of tools for validating these complexes, such as scoring functions, potential energy contributions, and med-chem features with ligand similarity estimates, and (3) powerful searching and filtering options with logical operators. DiSCuS supports multiple receptor targets for a single ligand, so it can be used either to evaluate different variants of an active site or for selectivity studies. DiSCuS documentation, installation instructions, and source code can be found at http://discus.ibb.waw.pl .

The evolutionary rate of antibacterial drug targets.

BACKGROUND: One of the major issues in the fight against infectious diseases is the notable increase in multiple drug resistance in pathogenic species. For that reason, newly acquired high-throughput data on virulent microbial agents attract the attention of many researchers seeking potential new drug targets. Many approaches have been used to evaluate proteins from infectious pathogens, including, but not limited to, similarity analysis, reverse docking, statistical 3D structure analysis, machine learning, topological properties of interaction networks or a combination of the aforementioned methods. From a biological perspective, most essential proteins (knockout lethal for bacteria) or highly conserved proteins (broad spectrum activity) are potential drug targets. Ribosomal proteins comprise such an example. Many of them are well-known drug targets in bacteria. It is intuitive that we should learn from nature how to design good drugs. Firstly, known antibiotics are mainly originating from natural products of microorganisms targeting other microorganisms. Secondly, paleontological data suggests that antibiotics have been used by microorganisms for million years. Thus, we have hypothesized that good drug targets are evolutionary constrained and are subject of evolutionary selection. This means that mutations in such proteins are deleterious and removed by selection, which makes them less susceptible to random development of resistance. Analysis of the speed of evolution seems to be good approach to test this hypothesis. RESULTS: In this study we show that pN/pS ratio of genes coding for known drug targets is significantly lower than the genome average and also lower than that for essential genes identified by experimental methods. Similar results are observed in the case of dN/dS analysis. Both analyzes suggest that drug targets tend to evolve slowly and that the rate of evolution is a better predictor of drugability than essentiality. CONCLUSIONS: Evolutionary rate can be used to score and find potential drug targets. The results presented here may become a useful addition to a repertoire of drug target prediction methods. As a proof of concept, we analyzed GO enrichment among the slowest evolving genes. These may become the starting point in the search for antibiotics with a novel mechanism.

A kinetic model of the evolution of a protein interaction network.

BACKGROUND: Known protein interaction networks have very particular properties. Old proteins tend to have more interactions than new ones. One of the best statistical representatives of this property is the node degree distribution (distribution of proteins having a given number of interactions). It has previously been shown that this distribution is very close to the sum of two distinct exponential components. In this paper, we asked: What are the possible mechanisms of evolution for such types of networks? To answer this question, we tested a kinetic model for simplified evolution of a protein interactome. Our proposed model considers the emergence of new genes and interactions and the loss of old ones. We assumed that there are generally two coexisting classes of proteins. Proteins constituting the first class are essential only for ecological adaptations and are easily lost when ecological conditions change. Proteins of the second class are essential for basic life processes and, hence, are always effectively protected against deletion. All proteins can transit between the above classes in both directions. We also assumed that the phenomenon of gene duplication is always related to ecological adaptation and that a new copy of a duplicated gene is not essential. According to this model, all proteins gain new interactions with a rate that preferentially increases with the number of interactions (the rich get richer). Proteins can also gain interactions because of duplication. Proteins lose their interactions both with and without the loss of partner genes. RESULTS: The proposed model reproduces the main properties of protein-protein interaction networks very well. The connectivity of the oldest part of the interaction network is densest, and the node degree distribution follows the sum of two shifted power-law functions, which is a theoretical generalization of the previous finding. The above distribution covers the wide range of values of node degrees very well, much better than a power law or generalized power law supplemented with an exponential cut-off. The presented model also relates the total number of interactome links to the total number of interacting proteins. The theoretical results were for the interactomes of A. thaliana, B. taurus, C. elegans, D. melanogaster, E. coli, H. pylori, H. sapiens, M. musculus, R. norvegicus and S. cerevisiae. CONCLUSIONS: Using these approaches, the kinetic parameters could be estimated. Finally, the model revealed the evolutionary kinetics of proteome formation, the phenomenon of protein differentiation and the process of gaining new interactions.

High-throughput sequencing identification of novel and conserved miRNAs in the Brassica oleracea leaves.

BACKGROUND: Plant microRNAs are short (~21 nt) non-coding molecules that regulate gene expression by targeting the mRNA cleavage or protein translation inhibition. In this manner, they play many important roles in the cells of living organisms. One of the plant species in which the entire set of miRNAs has not been yet completely identified is Brassica oleracea var. capitata (cabbage). For this reason and for the economic and nutritional importance of this food crop, high-throughput small RNAs sequencing has been performed to discover the novel and conserved miRNAs in mature cabbage leaves. RESULTS: In this study, raw reads generated from three small RNA libraries were bioinformatically processed and further analyzed to select sequences homologous to known B. oleracea and other plant miRNAs. As a result of this analysis, 261 conserved miRNAs (belonging to 62 families) have been discovered. MIR169, MIR167 and MIR166 were the largest miRNA families, while the highest abundance molecules were miR167, miR166, miR168c and miR157a. Among the generated sequencing reads, miRNAs* were also found, such as the miR162c*, miR160a* and miR157a*. The unannotated tags were used in the prediction and evaluation of novel miRNAs, which resulted in the 26 potential miRNAs proposal. The expressions of 13 selected miRNAs were analyzed by northern blot hybridization. The target prediction and annotation for identified miRNAs were performed, according to which discovered molecules may target mRNAs encoding several potential proteins - e.g., transcription factors, polypeptides that regulate hormone stimuli and abiotic stress response, and molecules participating in transport and cell communication. Additionally, KEGG maps analysis suggested that the miRNAs in cabbage are involved in important processing pathways, including glycolysis, glycerolipid metabolism, flavonoid biosynthesis and oxidative phosphorylation. CONCLUSIONS: Conclusively, for the first time, the large set of miRNAs was identified in mature cabbage leaves. Potential targets designation for these miRNAs may suggest their essential role in many plants primary biological processes. Presented study not only supplements the knowledge about B. oleracea miRNAs, but additionally it may be used in other research concerning the improvement of the cabbage cultivation.