Metabolomics, a Powerful Tool for Understanding Plant Abiotic Stress

Metabolomics is a technology that generates large amounts of data and contributes to obtaining wide and integral explanations of the biochemical state of a living organism. Plants are continuously affected by abiotic stresses such as water scarcity, high temperatures and high salinity, and metabolomics has the potential for elucidating the response-to-stress mechanisms and develop resistance strategies in affected cultivars. This review describes the characteristics of each of the stages of metabolomic studies in plants and the role of metabolomics in the characterization of the response of various plant species to abiotic stresses.

Sample-to-Analysis Platform for Rapid Intracellular Mass Spectrometry from Small Numbers of Cells

Real-time, advanced diagnostics of the biochemical state within cells remains a significant challenge for research and development, production, and application of cell-based therapies. The fundamental biochemical processes and mechanisms of...

Molecular Characterisation of Bacterial Isolates from Vegetable, Cow Milk and Locust Bean Samples

Molecular characterization involves characterization at molecular level without any effect of environment or development or physiological state of the organism. Biochemical characterization is the characterization of the biochemical state of the organism, which is in fact affected by environment, development as well as physiological state. The objective of this study was to molecularly characterize bacteria isolated from certain food samples. Five bacteria isolates were obtained from the vegetable samples while two LAB isolates were obtained from cow milk and locust bean samples. The bacteria isolates were identified using 16SRNA GENE sequencing using the BLAST algorithm and were identified as Staphylococcus aureus CIP 9973; Pectobacterium carotovorum subsp. carotovorum Pec 1; Enterobacter cloacae AS10 Klebsiella aerogenes OFM28; Escherichia coli 2013C-3342; Proteus mirabilis UPMSD3; Lactobacillus plantarum NCU116; Lactobacillus plantarum NRIC 0383. Characterization of bacteria isolates to molecular level is of enormous advantage as it helps to know the exact genus of a particular organism.

Mechanisms of signalling-memory governing progression through the eukaryotic cell cycle

As cells pass through each replication-division cycle, they must be able to postpone further progression if they detect any threats to genome integrity, such as DNA damage or misaligned chromosomes. Once a ‘decision’ is made to proceed, the cell unequivocally enters into a qualitatively different biochemical state, which makes the transitions from one cell cycle phase to the next switch-like and irreversible. Each transition is governed by a unique signalling network; nonetheless, they share a common characteristic of bistable behaviour, a hallmark of molecular memory devices. Comparing the cell cycle signalling mechanisms acting at the Restriction Point, G1/S, G2/M and meta-to-anaphase transitions, we deduce a generic network motif of coupled positive and negative feedback loops underlying each transition.

Microtubules (MT) a key target in oncology: mathematical modeling of anti-MT agents on cell migration

Microtubules (MTs) are protein filaments found in all eukaryotic cells which are crucial for many cellular processes including cell movement, cell differentiation, and cell division, making them a key target for anti-cancer treatment. In particular, it has been shown that at low dose, MT targeted agents (MTAs) may induce an anti-migratory effect on cancer and endothelial cells, leading to new prospects in cancer therapy. In that context, we propose to better understand the role of MT dynamics and thus of MTAs on cell migration using a mathematical cell centered model of cell migration taking into account the action of microtubules in the process. The model use a fluid based approach that describes, through level-set techniques, the deformation of the membrane during cell migration. The fluid part of the model is mainly composed of Stokes equations and the biochemical state of the cell is described using Reaction-Diffusion equations. Microtubules act on the biochemical state by activating or inactivating proteins of the Rho-GTPases family. The numerical simulation of the model is performed using Discrete Duality Finite Volume techniques. We describe the different schemes used for the simulation, focusing on the adaptation of preexisting methods to our particular case. Numerical simulation are performed, showing a realistic behavior of the simulated cells in term of shape, speed and microtubules dynamics. Different strategies for a depolymerizing MTA (Vincristin) mechanisms are investigated and show the robutness of our model.

Long-lived metabolic enzymes in the crystalline lens identified by pulse-labeling of mice and mass spectrometry

The lenticular fiber cells are comprised of extremely long-lived proteins while still maintaining an active biochemical state. Dysregulation of these activities has been implicated in diseases such as age-related cataracts. However, the lenticular protein dynamics underlying health and disease is unclear. We sought to measure the global protein turnover rates in the eye using nitrogen-15 labeling of mice and mass spectrometry. We measured the 14N/15N-peptide ratios of 248 lens proteins, including Crystallin, Aquaporin, Collagen and enzymes that catalyze glycolysis and oxidation/reduction reactions. Direct comparison of lens cortex versus nucleus revealed little or no 15N-protein contents in most nuclear proteins, while there were a broad range of 14N/15N ratios in cortex proteins. Unexpectedly, like Crystallins, many enzymes with relatively high abundance in nucleus were also exceedingly long-lived. The slow replacement of these enzymes in spite of young age of mice suggests their potential roles in age-related metabolic changes in the lens.

Free-energy-based framework for early forecasting of stem cell differentiation

Commitment of stem cells to different lineages is inherently stochastic but regulated by a range of environmental bio/chemo/mechanical cues. Here, we develop an integrated stochastic modelling framework for predicting the differentiation of hMSCs in response to a range of environmental cues, including sizes of adhesive islands, stiffness of substrates and treatment with ROCK inhibitors in both growth and mixed media. The statistical framework analyses the fluctuations of cell morphologies over approximately a 24 h period after seeding the cells in the specific environment and uses the cytoskeletal free-energy distribution to forecast the lineage the hMSCs will commit to. The cytoskeletal free energy which succinctly parametrizes the biochemical state of the cell is shown to capture hMSC commitment over a range of environments while simple morphological factors such as cell shape, tractions on their own are unable to correlate with lineages hMSCs adopt.