ICA 121431

Deletion of mazF increases Staphylococcus aureus biofilm formation in an ica-dependent manner.[Pubmed:28334216]

Pathog Dis. 2017 Jul 31;75(5). pii: 3063887.

Toxin-antitoxin (TA) systems are composed of a toxin that inhibits an essential cellular process (e.g. DNA replication, transcription, membrane integrity) and its cognate antitoxin that neutralizes the effect of the toxin. Staphylococcus aureus harbors two types of chromosomally encoded TA systems, namely mazEFsa encoding a UACAU-specific mRNA interferase and two paralogous genes of yefM-yoeBsa encoding a ribosome-dependent endoribonuclease system. However, little is known about the physiological role of MazEFsa and YefM-YoeBsa in S. aureus. Upon characterizing the phenotypes of single, double and triple gene deletion mutants, we found that mazFsa deletion led to increased biofilm formation. Subsequently, transcriptional analysis revealed that expression of intercellular adhesin (ica) gene, icaADBC, increased in a mazFsa deletion mutant. mazFsa/icaADBC double gene deletion and genetic complementation approaches provided convincing evidence that increased biofilm formation was caused by an increase in polysaccharide intercellular adhesin synthesized by icaADBC-encoded proteins. Furthermore, through the use of alanine substitutions at the conserved active residues of MazFsa, our results suggested that ica-mediated biofilm formation depended on the mRNA interferase activity of MazFsa. These findings give new insights not only into the physiological role of MazEFsa in S. aureus, but also into the regulatory mechanism of ica-dependent biofilm formation.

Multicomponent quantitative spectroscopic analysis without reference substances based on ICA modelling.[Pubmed:28299416]

Anal Bioanal Chem. 2017 May;409(13):3319-3327.

A fast and reliable spectroscopic method for multicomponent quantitative analysis of targeted compounds with overlapping signals in complex mixtures has been established. The innovative analytical approach is based on the preliminary chemometric extraction of qualitative and quantitative information from UV-vis and IR spectral profiles of a calibration system using independent component analysis (ICA). Using this quantitative model and ICA resolution results of spectral profiling of "unknown" model mixtures, the absolute analyte concentrations in multicomponent mixtures and authentic samples were then calculated without reference solutions. Good recoveries generally between 95% and 105% were obtained. The method can be applied to any spectroscopic data that obey the Beer-Lambert-Bouguer law. The proposed method was tested on analysis of vitamins and caffeine in energy drinks and aromatic hydrocarbons in motor fuel with 10% error. The results demonstrated that the proposed method is a promising tool for rapid simultaneous multicomponent analysis in the case of spectral overlap and the absence/inaccessibility of reference materials.

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.[Pubmed:28322859]

J Neurosci Methods. 2017 Apr 15;282:81-94.

BACKGROUND: Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. NEW METHOD: The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. RESULTS AND COMPARISON WITH EXISTING METHOD: The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p<0.001). Lower classification accuracy occurred when the extracted spatial maps contained more CSF regions (p<0.001). CONCLUSION: The success of sparse coding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations.

Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder.[Pubmed:28294459]

Hum Brain Mapp. 2017 May;38(5):2683-2708.

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683-2708, 2017. (c) 2017 Wiley Periodicals, Inc.

Voltage sensor interaction site for selective small molecule inhibitors of voltage-gated sodium channels.[Pubmed:23818614]

Proc Natl Acad Sci U S A. 2013 Jul 16;110(29):E2724-32.

Voltage-gated sodium (Nav) channels play a fundamental role in the generation and propagation of electrical impulses in excitable cells. Here we describe two unique structurally related nanomolar potent small molecule Nav channel inhibitors that exhibit up to 1,000-fold selectivity for human Nav1.3/Nav1.1 (ICA-121431, IC50, 19 nM) or Nav1.7 (PF-04856264, IC50, 28 nM) vs. other TTX-sensitive or resistant (i.e., Nav1.5) sodium channels. Using both chimeras and single point mutations, we demonstrate that this unique class of sodium channel inhibitor interacts with the S1-S4 voltage sensor segment of homologous Domain 4. Amino acid residues in the "extracellular" facing regions of the S2 and S3 transmembrane segments of Nav1.3 and Nav1.7 seem to be major determinants of Nav subtype selectivity and to confer differences in species sensitivity to these inhibitors. The unique interaction region on the Domain 4 voltage sensor segment is distinct from the structural domains forming the channel pore, as well as previously characterized interaction sites for other small molecule inhibitors, including local anesthetics and TTX. However, this interaction region does include at least one amino acid residue [E1559 (Nav1.3)/D1586 (Nav1.7)] that is important for Site 3 alpha-scorpion and anemone polypeptide toxin modulators of Nav channel inactivation. The present study provides a potential framework for identifying subtype selective small molecule sodium channel inhibitors targeting interaction sites away from the pore region.

ICA-121431 is a nanomolar potent and broad-spectrum voltage-gated sodium channel (Nav) blocker, shows equipotent selectivity for human Nav1.1 and Nav1.3 subtypes with IC50 values of 13 nM and 23 nM, respectively. ICA-121431 shows less potent inhibition of Nav1.2 (IC50=240 nM) and 1,000 fold selectivity against Nav1.4, Nav1.6, and the TTX-resistant human Nav1.5 and Nav1.8 channels (IC50s >10 µM).

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