Endogenously generated DNA nucleobase modifications source,and significance as possible biomarkers of malignant transformation risk,and role in anticancer therapy

The DNA of all living cells undergoes continuous structural and chemical alteration, which may be derived from exogenous sources, or endogenous, metabolic pathways, such as cellular respiration, replication and DNA demethylation. It has been estimated that approximately 70,000 DNA lesions may be generated per day in a single cell, and this has been linked to a wide variety of diseases, including cancer. However, it is puzzling why potentially mutagenic DNA modifications, occurring at a similar level in different organs/tissue, may lead to organ/tissue specific cancers, or indeed non-malignant disease – what is the basis for this differential response? We suggest that it is perhaps the precise location of damage, within the genome, that is a key factor. Finally, we draw attention to the requirement for reliable methods for identification and quantification of DNA adducts/modifications, and stress the need for these assays to be fully validated. Once these prerequisites are satisfied, measurement of DNA modifications may be helpful as a clinical parameter for treatment monitoring, risk group identification and development of prevention strategies.     

On the path to extinction: Inbreeding and admixture in a declining grey wolf population

Allee effects reduce the viability of small populations in many different ways, which act synergistically to lead populations towards extinction vortexes. The Sierra Morena wolf population, isolated in the south of the Iberian Peninsula and composed of just one or few packs for decades, represents a good example of how diverse threats act additively in very small populations. We sequenced the genome of one of the last wolves identified (and road‐killed) in Sierra Morena and that of another wolf in the Iberian Wolf Captive Breeding Program and compared them with other wolf and dog genomes from around the world (including two previously published genome sequences from northern Iberian wolves). The results showed relatively low overall genetic diversity in Iberian wolves, but diverse population histories including past introgression of dog genes. The Sierra Morena wolf had an extraordinarily high level of inbreeding and long runs of homozygosity, resulting from the long isolation. In addition, about one‐third of the genome was of dog origin. Despite the introgression of dog genes, heterozygosity remained low because of continued inbreeding after several hybridization events. The results thus illustrate the case of a small and isolated wolf population where the low population density may have favoured hybridization and introgression of dog alleles, but continued inbreeding may have resulted in large chromosomal fragments of wolf origin completely disappearing from the population, and being replaced by chromosomal fragments of dog origin. The latest population surveys suggest that this population may have gone extinct.     

Surface‐Modified Porous Carbon Nitride Composites as Highly Efficient Electrocatalyst for Zn‐Air Batteries

Porous carbon nitride (PCN) composites are fabricated using a top‐down strategy, followed by additions of graphene and CoS_x nanoparticles. This subsequently enhances conductivity and catalytic activity of PCN (abbreviated as CoS_x@PCN/rGO) and is achieved by one‐step sulfuration of PCN/graphene oxides (GO) composite materials. As a result, the as‐prepared CoS_x@PCN/rGO catalysts display excellent activity and stability toward both oxygen evolution and reduction reactions, surpassing electrocatalytic performance shown by state‐of‐the‐art Pt, RuO₂ and other carbon nitrides. Remarkably, the CoS_x@PCN/rGO bifunctional activity allows for applications in zinc‐air batteries, which show better rechargeability than Pt/C. The enhanced catalytic performance of CoS_x@PCN/rGO can primarily be attributed to the highly porous morphology and sufficiently exposed active sites that are favorable for electrocatalytic reactions.     

Toxicological Aspects of the Essential Oil from Cinnamodendron dinisii

The objective of this study was to determine cytotoxic activity, hemolytic activity, and to evaluate the ability of the essential oil from Cinnamodendron dinisii to induce DNA fragmentation of human lymphocytes. The essential oil was obtained by hydrodistillation. Cytotoxic activity was determined by the MTT method. Hemolytic activity was evaluated by spectrophotometric quantification of hemoglobin released by erythrocytes. Damage to lymphocyte DNA molecules was assessed by the Comet assay. The essential oil under study showed high cytotoxic activity on Vero cells (CC₅₀ = 35.72 μg/mL) and induced hemolysis in both hematocrits, besides leading to the oxidation of hemoglobin released. The genotoxic activity of C. dinisii essential oil was also observed, which induced concentration‐dependent DNA fragmentation of human lymphocytes and, at 50 μL/mL, it was more active than the positive control. The essential oil from C. dinisii has a toxic action, suggesting a special attention in the application of this oil to health‐promoting activities; however, among its components, there are molecules with potential for future application in anticancer therapies.     

A machine‐learning approach for extending classical wildlife resource selection analyses

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.     

A 15.6 frames per second 1‐megapixel multiple exposure laser speckle contrast imaging setup

A multiple exposure laser speckle contrast imaging (MELSCI) setup for visualizing blood perfusion was developed using a field programmable gate array (FPGA), connected to a 1000 frames per second (fps) 1‐megapixel camera sensor. Multiple exposure time images at 1, 2, 4, 8, 16, 32 and 64 milliseconds were calculated by cumulative summation of 64 consecutive snapshot images. The local contrast was calculated for all exposure times using regions of 4 × 4 pixels. Averaging of multiple contrast images from the 64‐millisecond acquisition was done to improve the signal‐to‐noise ratio. The results show that with an effective implementation of the algorithm on an FPGA, contrast images at all exposure times can be calculated in only 28 milliseconds. The algorithm was applied to data recorded during a 5 minutes finger occlusion. Expected contrast changes were found during occlusion and the following hyperemia in the occluded finger, while unprovoked fingers showed constant contrast during the experiment. The developed setup is capable of massive data processing on an FPGA that enables processing of MELSCI data in 15.6 fps (1000/64 milliseconds). It also leads to improved frame rates, enhanced image quality and enables the calculation of improved microcirculatory perfusion estimates compared to single exposure time systems.      

Elongator—a tRNA modifying complex that promotes efficient translational decoding

Naturally occurring modifications of the nucleosides in the anticodon region of tRNAs influence their translational decoding properties. Uridines present at the wobble position in eukaryotic cytoplasmic tRNAs often contain a 5-carbamoylmethyl (ncm⁵) or 5-methoxycarbonylmethyl (mcm⁵) side-chain and sometimes also a 2-thio or 2′-O-methyl group. The first step in the formation of the ncm⁵ and mcm⁵ side-chains requires the conserved six-subunit Elongator complex. Although Elongator has been implicated in several different cellular processes, accumulating evidence suggests that its primary, and possibly only, cellular function is to promote modification of tRNAs. In this review, we discuss the biosynthesis and function of modified wobble uridines in eukaryotic cytoplasmic tRNAs, focusing on the in vivo role of Elongator-dependent modifications in Saccharomyces cerevisiae. This article is part of a Special Issue entitled: SI: Regulation of tRNA synthesis and modification in physiological conditions and disease edited by Dr. Boguta Magdalena.