p66Shc Protein-Oxidative Stress Sensor or Redox Enzyme: Its Potential Role in Mitochondrial Metabolism of Human Breast Cancer.

This work presents a comprehensive evaluation of the role of p66Shc protein in mitochondrial physiology in MDA-MB-231 breast cancer cells. The use of human breast cancer cell line MDA-MB-231 and its genetically modified clones (obtained with the use of the CRISPR-Cas9 technique), expressing different levels of p66Shc protein, allowed us to demonstrate how the p66Shc protein affects mitochondrial metabolism of human breast cancer cells. Changes in the level of p66Shc (its overexpression, and overexpressing of its Serine 36-mutated version, as well as the knockout of p66Shc) exert different effects in breast cancer cells. Interestingly, knocking out p66Shc caused significant changes observed mostly in mitochondrial bioenergetic parameters. We have shown that an MDA-MB-231 (which is a strong metastatic type of breast cancer) clone lacking p66Shc protein is characterized by a significant shift in the metabolic phenotype in comparison to other MDA-MB-231 clones. Additionally, this clone is significantly more vulnerable to doxorubicin treatment. We have proved that p66Shc adaptor protein in human breast cancer cells may exert a different role than in noncancerous cells (e.g., fibroblasts).

Analysis of Deposition and Diffusion of Cholesterol in Silicone Hydrogel Contact Lenses Using Confocal Microscopy.

In this study, we investigated lipid deposition and diffusion in silicone hydrogel (Si-Hy) contact lenses using confocal microscopy. Different Si-Hy lenses were analyzed to understand the interaction patterns of cholesterol with various lens materials. The results highlight significant differences in the deposition and diffusion of lipids through the lenses, revealing that some materials, such as comfilcon A, allow lipids to diffuse more freely compared to others, such as samfilcon A, which provides a greater barrier. The study also observed different morphology and movement of lipid agglomerates across the lenses and above it surfaces. These findings contribute to the understanding of lipid-lens interaction, which is important for the development of lenses with improved comfort and functionality. The research highlights the importance of considering lipid interactions in the design and selection of Si-Hy contact lenses to enhance wearer comfort and lens performance.

Mechanical forces orchestrate the metabolism of the developing oilseed rape embryo.

The initial free expansion of the embryo within a seed is at some point inhibited by its contact with the testa, resulting in its formation of folds and borders. Although less obvious, mechanical forces appear to trigger and accelerate seed maturation. However, the mechanistic basis for this effect remains unclear. Manipulation of the mechanical constraints affecting either the in vivo or in vitro growth of oilseed rape embryos was combined with analytical approaches, including magnetic resonance imaging and computer graphic reconstruction, immunolabelling, flow cytometry, transcriptomic, proteomic, lipidomic and metabolomic profiling. Our data implied that, in vivo, the imposition of mechanical restraints impeded the expansion of testa and endosperm, resulting in the embryo's deformation. An acceleration in embryonic development was implied by the cessation of cell proliferation and the stimulation of lipid and protein storage, characteristic of embryo maturation. The underlying molecular signature included elements of cell cycle control, reactive oxygen species metabolism and transcriptional reprogramming, along with allosteric control of glycolytic flux. Constricting the space allowed for the expansion of in vitro grown embryos induced a similar response. The conclusion is that the imposition of mechanical constraints over the growth of the developing oilseed rape embryo provides an important trigger for its maturation.

Nuclear Rac1 controls nuclear architecture and cell migration of glioma cells.

Rac1 (Ras-related C3 botulinum toxin substrate 1) protein has been found in the cell nucleus many years ago, however, its nuclear functions are still poorly characterized but some data suggest its nuclear accumulation in cancers. We investigated nuclear Rac1 in glioma cancer cells nuclei and compared its levels and activity to normal astrocytes, and also characterized the studied cells on various nuclear properties and cell migration patterns. Nuclear Rac1 indeed was found accumulated in glioma cells, but only a small percentage of the protein was in active, GTP-bound state in comparison to healthy control. Altering the nuclear activity of Rac1 influenced chromatin architecture and cell motility in GTP-dependent and independent manner. This suggests that the landscape of Rac1 nuclear interactions might be as complicated and wide as its well-known, non-nuclear signaling.

Deep learning the cis-regulatory code for gene expression in selected model plants.

Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predicting gene expression profiles from gene flanking regions of the plant species Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. With over 80% accuracy, our models enabled predictive feature selection, highlighting e.g. the significant role of UTR regions in determining gene expression levels. The models demonstrated remarkable cross-species performance, effectively identifying both conserved and species-specific regulatory sequence features and their predictive power for gene expression. We illustrated the application of our approach by revealing causal links between genetic variation and gene expression changes across fourteen tomato genomes. Lastly, our models efficiently predicted genotype-specific expression of key functional gene groups, exemplified by underscoring known phenotypic and metabolic differences between Solanum lycopersicum and its wild, drought-resistant relative, Solanum pennellii.

Dynamic adaptation of the extremophilic red microalga Cyanidioschyzon merolae to high nickel stress.

The order of Cyanidiales comprises seven acido-thermophilic red microalgal species thriving in hot springs of volcanic origin characterized by extremely low pH, moderately high temperatures and the presence of high concentrations of sulphites and heavy metals that are prohibitive for most other organisms. Little is known about the physiological processes underlying the long-term adaptation of these extremophiles to such hostile environments. Here, we investigated the long-term adaptive responses of a red microalga Cyanidioschyzon merolae, a representative of Cyanidiales, to extremely high nickel concentrations. By the comprehensive physiological, microscopic and elemental analyses we dissected the key physiological processes underlying the long-term adaptation of this model extremophile to high Ni exposure. These include: (i) prevention of significant Ni accumulation inside the cells; (ii) activation of the photoprotective response of non-photochemical quenching; (iii) significant changes of the chloroplast ultrastructure associated with the formation of prolamellar bodies and plastoglobuli together with loosening of the thylakoid membranes; (iv) activation of ROS amelioration machinery; and (v) maintaining the efficient respiratory chain functionality. The dynamically regulated processes identified in this study are discussed in the context of the mechanisms driving the remarkable adaptability of C. merolae to extremely high Ni levels exceeding by several orders of magnitude those found in the natural environment of the microalga. The processes identified in this study provide a solid basis for the future investigation of the specific molecular components and pathways involved in the adaptation of Cyanidiales to the extremely high Ni concentrations.

Integrated multi-omics analyses and genome-wide association studies reveal prime candidate genes of metabolic and vegetative growth variation in canola.

Genome-wide association studies (GWAS) identified thousands of genetic loci associated with complex plant traits, including many traits of agronomical importance. However, functional interpretation of GWAS results remains challenging because of large candidate regions due to linkage disequilibrium. High-throughput omics technologies, such as genomics, transcriptomics, proteomics and metabolomics open new avenues for integrative systems biological analyses and help to nominate systems information supported (prime) candidate genes. In the present study, we capitalise on a diverse canola population with 477 spring-type lines which was previously analysed by high-throughput phenotyping of growth-related traits and by RNA sequencing and metabolite profiling for multi-omics-based hybrid performance prediction. We deepened the phenotypic data analysis, now providing 123 time-resolved image-based traits, to gain insight into the complex relations during early vegetative growth and reanalysed the transcriptome data based on the latest Darmor-bzh v10 genome assembly. Genome-wide association testing revealed 61 298 robust quantitative trait loci (QTL) including 187 metabolite QTL, 56814 expression QTL and 4297 phenotypic QTL, many clustered in pronounced hotspots. Combining information about QTL colocalisation across omics layers and correlations between omics features allowed us to discover prime candidate genes for metabolic and vegetative growth variation. Prioritised candidate genes for early biomass accumulation include A06p05760.1_BnaDAR (PIAL1), A10p16280.1_BnaDAR, C07p48260.1_BnaDAR (PRL1) and C07p48510.1_BnaDAR (CLPR4). Moreover, we observed unequal effects of the Brassica A and C subgenomes on early biomass production.

Study of silicone hydrogel contact lenses' surface reflection characteristics using confocal microscopy.

The physical and chemical properties of contact lenses (CLs) differ significantly from one another. This is already covered by the FDA classification, which divides soft lenses into groups and subgroups for additional characteristics. The differences relate to both the interior and surface of the lens. Several differences in the surface characteristics of individual contact lenses have been studied and demonstrated to date. However, one of their fundamental physical properties, that is light reflection or, quantitatively, reflectance has not been compared. This paper describes the surface differences of a range of silicone-hydrogel (SiHy) lenses using reflectance confocal microscopy. It shows the relationship between the amount of light reflected from the lens surface and the material parameters. Common SiHy lens materials were used in the study, including two lenses with surface modifications. Light incident at the interface between two media (phosphate-buffered saline and lens) with different refractive indices is partially reflected. The normalized results show significant differences between the reflection signals (1 vs 0.07), and that they are not correlated with the refractive index (R2 = 0.5536). For the water content (%H2O), a general trend was observed that the higher the %H2O, the lower the reflection signal is (R2 = 0.8105). The reflection signal and surface modulus show the best correlation. (R2 = 0.9883). The proposed CLs analysis method, using reflectance confocal microscopy, provides data to differentiate between lenses with and without surface modifications.

Influence of Yokukansan on the refractive index of neuroblastoma cells.

Yokukansan (YKS) is a traditional Japanese herbal medicine that is increasingly being studied for its effects on neurodegenerative diseases. In our study, we presented a novel methodology for a multimodal analysis of the effects of YKS on nerve cells. The measurements of 3D refractive index distribution and its changes performed by holographic tomography were supported with an investigation by Raman micro-spectroscopy and fluorescence microscopy to gather complementary morphological and chemical information about cells and YKS influence. It was shown that at the concentrations tested, YKS inhibits proliferation, possibly involving reactive oxygen species. Also substantial changes in the cell RI after few hours of YKS exposure were detected, followed by longer-term changes in cell lipid composition and chromatin state.

Hello darkness, my old friend: 3-KETOACYL-COENZYME A SYNTHASE4 is a branch point in the regulation of triacylglycerol synthesis in Arabidopsis thaliana.

Plant lipids are important as alternative sources of carbon and energy when sugars or starch are limited. Here, we applied combined heat and darkness or extended darkness to a panel of ∼300 Arabidopsis (Arabidopsis thaliana) accessions to study lipid remodeling under carbon starvation. Natural allelic variation at 3-KETOACYL-COENZYME A SYNTHASE4 (KCS4), a gene encoding an enzyme involved in very long chain fatty acid (VLCFA) synthesis, underlies the differential accumulation of polyunsaturated triacylglycerols (puTAGs) under stress. Ectopic expression of KCS4 in yeast and plants proved that KCS4 is a functional enzyme localized in the endoplasmic reticulum with specificity for C22 and C24 saturated acyl-CoA. Allelic mutants and transient overexpression in planta revealed the differential role of KCS4 alleles in VLCFA synthesis and leaf wax coverage, puTAG accumulation, and biomass. Moreover, the region harboring KCS4 is under high selective pressure and allelic variation at KCS4 correlates with environmental parameters from the locales of Arabidopsis accessions. Our results provide evidence that KCS4 plays a decisive role in the subsequent fate of fatty acids released from chloroplast membrane lipids under carbon starvation. This work sheds light on both plant response mechanisms and the evolutionary events shaping the lipidome under carbon starvation.

SWEET11b transports both sugar and cytokinin in developing barley grains.

Even though Sugars Will Eventually be Exported Transporters (SWEETs) have been found in every sequenced plant genome, a comprehensive understanding of their functionality is lacking. In this study, we focused on the SWEET family of barley (Hordeum vulgare). A radiotracer assay revealed that expressing HvSWEET11b in African clawed frog (Xenopus laevis) oocytes facilitated the bidirectional transfer of not only just sucrose and glucose, but also cytokinin. Barley plants harboring a loss-of-function mutation of HvSWEET11b could not set viable grains, while the distribution of sucrose and cytokinin was altered in developing grains of plants in which the gene was knocked down. Sucrose allocation within transgenic grains was disrupted, which is consistent with the changes to the cytokinin gradient across grains, as visualized by magnetic resonance imaging and Fourier transform infrared spectroscopy microimaging. Decreasing HvSWEET11b expression in developing grains reduced overall grain size, sink strength, the number of endopolyploid endosperm cells, and the contents of starch and protein. The control exerted by HvSWEET11b over sugars and cytokinins likely predetermines their synergy, resulting in adjustments to the grain's biochemistry and transcriptome.

Metabolic diversity in a collection of wild and cultivated Brassica rapa subspecies.

Brassica rapa (B. rapa) and its subspecies contain many bioactive metabolites that are important for plant defense and human health. This study aimed at investigating the metabolite composition and variation among a large collection of B. rapa genotypes, including subspecies and their accessions. Metabolite profiling of leaves of 102 B. rapa genotypes was performed using ultra-performance liquid chromatography coupled with a photodiode array detector and quadrupole time-of-flight mass spectrometry (UPLC-PDA-QTOF-MS/MS). In total, 346 metabolites belonging to different chemical classes were tentatively identified; 36 out of them were assigned with high confidence using authentic standards and 184 were those reported in B. rapa leaves for the first time. The accumulation and variation of metabolites among genotypes were characterized and compared to their phylogenetic distance. We found 47 metabolites, mostly representing anthocyanins, flavonols, and hydroxycinnamic acid derivatives that displayed a significant correlation to the phylogenetic relatedness and determined four major phylometabolic branches; 1) Chinese cabbage, 2) yellow sarson and rapid cycling, 3) the mizuna-komatsuna-turnip-caitai; and 4) a mixed cluster. These metabolites denote the selective pressure on the metabolic network during B. rapa breeding. We present a unique study that combines metabolite profiling data with phylogenetic analysis in a large collection of B. rapa subspecies. We showed how selective breeding utilizes the biochemical potential of wild B. rapa leading to highly diverse metabolic phenotypes. Our work provides the basis for further studies on B. rapa metabolism and nutritional traits improvement.

From data to knowledge - big data needs stewardship, a plant phenomics perspective.

The research data life cycle from project planning to data publishing is an integral part of current research. Until the last decade, researchers were responsible for all associated phases in addition to the actual research and were assisted only at certain points by IT or bioinformaticians. Starting with advances in sequencing, the automation of analytical methods in all life science fields, including in plant phenotyping, has led to ever-increasing amounts of ever more complex data. The tasks associated with these challenges now often exceed the expertise of and infrastructure available to scientists, leading to an increased risk of data loss over time. The IPK Gatersleben has one of the world's largest germplasm collections and two decades of experience in crop plant research data management. In this article we show how challenges in modern, data-driven research can be addressed by data stewards. Based on concrete use cases, data management processes and best practices from plant phenotyping, we describe which expertise and skills are required and how data stewards as an integral actor can enhance the quality of a necessary digital transformation in progressive research.

Method to analyze effects of low-level laser therapy on biological cells with a digital holographic microscope.

Low-level laser therapy (LLLT) is a therapeutic tool that uses the photobiochemical interaction between light and tissue. Its effectiveness is controversial due to a strong dependence on dosimetric parameters. In this work, we demonstrate that digital holographic microscopy is an effective label-free imaging technique to analyze the effects of LLLT on biological cells, and we propose the full methodology to create correct synthetic aperture phase maps for further extensive, highly accurate statistical analysis. The proposed methodology has been designed to provide a basis for many other biological experiments using quantitative phase imaging. We use SHSY-5Y and HaCaT cells irradiated with different doses of red light for the experiment. The analysis shows quantitative changes in cell dry mass density and the projected cell surface in response to different radiation doses.

Carbon flux through photosynthesis and central carbon metabolism show distinct patterns between algae, C3 and C4 plants.

Photosynthesis-related pathways are regarded as a promising avenue for crop improvement. Whilst empirical studies have shown that photosynthetic efficiency is higher in microalgae than in C3 or C4 crops, the underlying reasons remain unclear. Using a tailor-made microfluidics labelling system to supply 13CO2 at steady state, we investigated in vivo labelling kinetics in intermediates of the Calvin Benson cycle and sugar, starch, organic acid and amino acid synthesis pathways, and in protein and lipids, in Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii, which is the fastest growing green alga on record. We estimated flux patterns in these algae and compared them with published and new data from C3 and C4 plants. Our analyses identify distinct flux patterns supporting faster growth in photosynthetic cells, with some of the algae exhibiting faster ribulose 1,5-bisphosphate regeneration and increased fluxes through the lower glycolysis and anaplerotic pathways towards the tricarboxylic acid cycle, amino acid synthesis and lipid synthesis than in higher plants.

Steroidal alkaloids defence metabolism and plant growth are modulated by the joint action of gibberellin and jasmonate signalling.

Steroidal glycoalkaloids (SGAs) are protective metabolites constitutively produced by Solanaceae species. Genes and enzymes generating the vast structural diversity of SGAs have been largely identified. Yet, mechanisms of hormone pathways coordinating defence (jasmonate; JA) and growth (gibberellin; GA) controlling SGAs metabolism remain unclear. We used tomato to decipher the hormonal regulation of SGAs metabolism during growth vs defence tradeoff. This was performed by genetic and biochemical characterisation of different JA and GA pathways components, coupled with in vitro experiments to elucidate the crosstalk between these hormone pathways mediating SGAs metabolism. We discovered that reduced active JA results in decreased SGA production, while low levels of GA or its receptor led to elevated SGA accumulation. We showed that MYC1 and MYC2 transcription factors mediate the JA/GA crosstalk by transcriptional activation of SGA biosynthesis and GA catabolism genes. Furthermore, MYC1 and MYC2 transcriptionally regulate the GA signalling suppressor DELLA that by itself interferes in JA-mediated SGA control by modulating MYC activity through protein-protein interaction. Chemical and fungal pathogen treatments reinforced the concept of JA/GA crosstalk during SGA metabolism. These findings revealed the mechanism of JA/GA interplay in SGA biosynthesis to balance the cost of chemical defence with growth.

Serial Block-Face Scanning Electron Microscopy (SBEM) for the Study of Dendritic Spines.

Three-dimensional electron microscopy (3D EM) gives a possibility to analyze morphological parameters of dendritic spines with nanoscale resolution. In addition, some features of dendritic spines, such as volume of the spine and post-synaptic density (PSD) (representing post-synaptic part of the synapse), presence of presynaptic terminal, and smooth endoplasmic reticulum or atypical form of PSD (e.g., multi-innervated spines), can be observed only with 3D EM. By employing serial block-face scanning electron microscopy (SBEM) it is possible to obtain 3D EM data easier and in a more reproducible manner than when performing traditional serial sectioning. Here we show how to prepare mouse hippocampal samples for SBEM analysis and how this protocol can be combined with immunofluorescence study of dendritic spines. Mild fixation perfusion allows us to perform immunofluorescence studies with light microscopy on one half of the brain, while the other half was prepared for SBEM. This approach reduces the number of animals to be used for the study.

Advances in flux balance analysis by integrating machine learning and mechanism-based models.

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

Effects of plant alkaloids on mitochondrial bioenergetic parameters.

Mitochondria are among the first responders to various stress factors that challenge cell and tissue homeostasis. Various plant alkaloids have been investigated for their capacity to modulate mitochondrial activities. In this study, we used isolated mitochondria from mouse brain and liver tissues to assess nicotine, anatabine and anabasine, three alkaloids found in tobacco plant, for potential modulatory activity on mitochondrial bioenergetics parameters. All alkaloids decreased basal oxygen consumption of mouse brain mitochondria in a dose-dependent manner without any effect on the ADP-stimulated respiration. None of the alkaloids, at 1 nM or 1.25 µM concentrations, influenced the maximal rate of swelling of brain mitochondria. In contrast to brain mitochondria, 1.25 µM anatabine, anabasine and nicotine increased maximal rate of swelling of liver mitochondria suggesting a toxic effect. Only at 1 mM concentration, anatabine slowed down the maximal rate of Ca2+-induced swelling and increased the time needed to reach the maximal rate of swelling. The observed mitochondrial bioenergetic effects are probably mediated through a pathway independent of nicotinic acetylcholine receptors, as quantitative proteomic analysis could not confirm their expression in pure mitochondrial fractions isolated from mouse brain tissue.