Chemosensory Optode Array Based on Pluronic-Stabilized Microspheres for Differential Sensing

Differential sensing techniques are becoming nowadays an attractive alternative to classical selective recognition methods due to the “fingerprinting” possibility allowing identifying various analytes without the need to fabricate highly selective binding recognition sites. This work shows for the first time that surfactant-based ion-sensitive microspheres as optodes in the microscale can be designed as cross-sensitive materials; thus, they are perfect candidates as sensing elements for differential sensing. Four types of the newly developed chemosensory microspheres—anion- and cation-selective, sensitive toward amine- and hydroxyl moiety—exhibited a wide range of linear response (two to five orders of magnitude) in absorbance and/or fluorescence mode, great time stability (at least 2 months), as well as good fabrication repeatability. The array of four types of chemosensitive microspheres was capable of perfect pattern-based identification of eight neurotransmitters: dopamine, epinephrine, norepinephrine, γ-aminobutyric acid (GABA), acetylcholine, histamine, taurine, and phenylethylamine. Moreover, it allowed the quantification of neurotransmitters, also in mixtures. Its selectivity toward neurotransmitters was studied using α- and β-amino acids (Ala, Asp, Pro, Tyr, taurine) in simulated blood plasma solution. It was revealed that the chemosensory optode set could recognize subtle differences in the chemical structure based on the differential interaction of microspheres with various moieties present in the molecule. The presented method is simple, versatile, and convenient, and it could be adopted to various quantitative and qualitative analytical tasks due to the simple adjusting of microspheres components and measurement conditions.

MITOL-mediated DRP1 ubiquitylation and degradation promotes mitochondrial hyperfusion in CMT2A-linked MFN2 mutant

Mutations in Mitofusin2 (MFN2), associated with the pathology of the debilitating neuropathy, Charcot-Marie-Tooth type 2A (CMT2A) are known to alter mitochondrial morphology. One such abundant MFN2 mutant, R364W results in the generation of elongated, interconnected mitochondria. However, the mechanism leading to this mitochondrial aberration remains poorly understood. Here we show that mitochondrial hyperfusion in the presence of R364W-MFN2 is due to increased degradation of DRP1. The Ubiquitin E3 ligase MITOL is known to ubiquitylate both MFN2 and DRP1. Interaction with and its subsequent ubiquitylation by MITOL is stronger in presence of WT-MFN2 than R364W-MFN2. This differential interaction of MITOL with MFN2 in the presence of R364W-MFN2 renders the ligase more available for DRP1 ubiquitylation. Multimonoubiquitylation and proteasomal degradation of DRP1 in R364W-MFN2 cells in the presence of MITOL eventually leads to mitochondrial hyperfusion. Here we provide a mechanistic insight into mitochondrial hyperfusion, while also reporting that MFN2 can indirectly modulate DRP1 – an effect not shown before.

Difference of MreBCD Complex Interactome in Salmonella Typhimurium ST19 and ST213 Genotypes on Pathogenesis and Stress Response Pathways

: Salmonella enterica is the etiological agent of salmonellosis, with a high infection rate worldwide. In Mexico, ST213 genotype of S. enterica ser. Typhimurium is displacing the ancestral ST19 genotype. Bacterial cytoskeleton protein complex MreBCD play an important role in S. enterica pathogenesis, but underlying mechanisms are unknown. In this study, 106 interactions among MreBCD and 15 proteins from S. Typhimurium Pathogenicity Islands 1 (SP-I) and 2 (SP-2) involved in both bacterial virulence and stress response were predicted in ST213 and ST19 genotypes, of which 12 interactions were confirmed in vitro. In addition, gene cluster analysis in 100 S. Typhimurium genomes was performed for these genes. The in silico and in vitro results showed a novel MreBCD interactome involved in the regulation of pathogenesis and stress response through interactions with virulence factors located at SPI-1 and SPI-2. Furthermore, both pseudogene presence and sequence variations in four tested proteins between genotypes resulted in differential interaction patterns that are involved in Salmonella motility and survival in eukaryotic cells, which could explain replacement of ST19 by ST213 in Mexico.

Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity

Summary Growing evidence has shown that the brain connectivity network experiences alterations for complex diseases such as Alzheimer’s disease (AD). Network comparison, also known as differential network analysis, is thus particularly powerful to reveal the disease pathologies and identify clinical biomarkers for medical diagnoses (classification). Data from neurophysiological measurements are multidimensional and in matrix-form. Naive vectorization method is not sufficient as it ignores the structural information within the matrix. In the article, we adopt the Kronecker product covariance matrices framework to capture both spatial and temporal correlations of the matrix-variate data while the temporal covariance matrix is treated as a nuisance parameter. By recognizing that the strengths of network connections may vary across subjects, we develop an ensemble-learning procedure, which identifies the differential interaction patterns of brain regions between the case group and the control group and conducts medical diagnosis (classification) of the disease simultaneously. Simulation studies are conducted to assess the performance of the proposed method. We apply the proposed procedure to the functional connectivity analysis of an functional magnetic resonance imaging study on AD. The hub nodes and differential interaction patterns identified are consistent with existing experimental studies, and satisfactory out-of-sample classification performance is achieved for medical diagnosis of AD.

Identification of key proteins involved in stickleback environmental adaptation with system-level analysis

Using abundance measurements of 1,490 proteins from four separate populations of three-spined sticklebacks, we implemented a system-level approach to correlate proteome dynamics with environmental salinity and temperature and the fish's population and morphotype. We identified robust and accurate fingerprints that classify environmental salinity, temperature, morphotype, and the population sample origin, observing that proteins with specific functions are enriched in these fingerprints. Highly apparent functions represented in all fingerprints include ion transport, proteostasis, growth, and immunity, suggesting that these functions are most diversified in populations inhabiting different environments. Applying a differential network approach, we analyzed the network of protein interactions that differs between populations. Looking at specific population combinations of differential interaction, we identify sets of connected proteins. We find that these sets and their corresponding enriched functions reflect key processes that have diverged between the four populations. Moreover, the extent of divergence, i.e., the number of enriched functions that differ between populations, is highest when all three environmental parameters are different between two populations. Key nodes in the differential interaction network signify functions that are also inherent in the fingerprints, most prominently proteostasis-related functions. However, the differential interaction network also reveals additional functions that have diverged between populations, notably cytoskeletal organization and morphogenesis. The strength of these analyses is that the results are purely data driven. With such an unbiased approach applied on a large proteomic data set, we find the strongest signals given by the data, making it possible to develop more discriminatory and complex biomarkers for specific contexts of interest.

TSC-Insensitive Rheb Mutations Induce Oncogenic Transformation Through a Combination of Hyperactive mTORC1 Signalling and Metabolic Reprogramming

The mechanistic target of rapamycin complex 1 (mTORC1) is an important regulator of cellular metabolism that is commonly hyperactivated in cancer. Recent cancer genome screens have identified multiple mutations in Ras-homolog enriched in brain (Rheb), the primary activator of mTORC1, that might act as driver oncogenes by causing hyperactivation of mTORC1. Here, we show that a number of recurrently occurring Rheb mutants drive hyperactive mTORC1 signalling through differing levels of insensitivity to the primary inactivator of Rheb, Tuberous Sclerosis Complex.We show that two activated mutants, Rheb-T23M and E40K, strongly drive increased cell growth, proliferation and anchorage-independent growth resulting in enhanced tumour growth in vivo. Proteomic analysis of cells expressing the mutations revealed, surprisingly, that these two mutants promote distinct oncogenic pathways with Rheb-T23M driving metabolic reprogramming and an increased rate of glycolysis, while Rheb-E40K regulates the translation factor eEF2 and autophagy, likely through a differential interaction with AMPK.Our findings suggest that unique ‘bespoke’ combination therapies may be utilised to treat cancers according to which Rheb mutant they harbour.