Image_1_Metabonomic Profile and Signaling Pathway Prediction of Depression-Associated Suicidal Behavior.tiff

Suicide is the most severe consequence of depression which has become a leading cause of disability and a global disease burden. Recent evidence indicates a central role of small molecules in the pathogenesis of depression and associated suicidal behaviors. However, there lacks a systemic exploration of small molecules in the development of depression-associated suicide, and it remains unclear how they affect an individual’s behavior. In order to compare the metabonomic profiles between drug-naïve patients with depression-associated suicidal behaviors and healthy individuals, we conducted a systemic database search for studies of metabolic characteristics in depression-associated suicidal behavior. Manual data curation and statistical analysis and integration were performed in Excel. We further performed an enrichment analysis of signaling pathway prediction using the Reactome Pathway Analysis tool. We have identified 17 metabolites that expressed differently between drug-naïve patients with depression-associated suicidal behaviors and healthy controls. We have integrated these metabolites into biological signaling pathways and provided a visualized signaling network in depressed suicidal patients. We have revealed that “transport of small molecules”, “disease”, “metabolism” and “metabolism of proteins” were the most relevant signaling sections, among which “transport of inorganic cations/anions and amino acids/oligopeptides”, “SLC-mediated transmembrane transport”, and “metabolism of amino acids and derivatives” should be further studied to elucidate their potential pathogenic mechanism in the development of depression and associated suicidal behavior. In conclusion, our findings of these 17 metabolites and associated signaling pathways could provide an insight into the molecular pathogenesis of depression-associated suicidal behavior and potential targets for new drug inventions.</p

The success rate for recognizing proteins within the same family, superfamily, or fold in the Lindahl benchmark.

a<p>The percentage in each cell is the fraction of correctly recognized match of proteins in the same fold, super family, and family as first rank or within top 5 rank of the template .</p>b<p>From Ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0002325#pone.0002325-Zhou2" target="_blank">[10]</a>.</p>c<p>From Ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0002325#pone.0002325-Cheng1" target="_blank">[48]</a>.</p>d<p>From Ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0002325#pone.0002325-Zhou3" target="_blank">[11]</a>.</p>e<p>From Ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0002325#pone.0002325-Liu1" target="_blank">[12]</a>.</p>f<p>This work.</p>g<p>This work (The 43 proteins with >30% sequence similarity to PREFAB training set are removed).</p>h<p>The standard error was estimated by bootstrap simulation on 10,000 re-sampling of the data set.</p

Fast and Accurate Method for Identifying High-Quality Protein-Interaction Modules by Clique Merging and Its Application to Yeast

Molecular networks in cells are organized into functional modules, where genes in the same module interact densely with each other and participate in the same biological process. Thus, identification of modules from molecular networks is an important step toward a better understanding of how cells function through the molecular networks. Here, we propose a simple, automatic method, called MC2, to identify functional modules by enumerating and merging cliques in the protein-interaction data from large-scale experiments. Application of MC2 to the S. cerevisiae protein-interaction data produces 84 modules, whose sizes range from 4 to 69 genes. The majority of the discovered modules are significantly enriched with a highly specific process term (at least 4 levels below root) and a specific cellular component in Gene Ontology (GO) tree. The average fraction of genes with the most enriched GO term for all modules is 82% for specific biological processes and 78% for specific cellular components. In addition, the predicted modules are enriched with coexpressed proteins. These modules are found to be useful for annotating unknown genes and uncovering novel functions of known genes. MC2 is efficient, and takes only about 5 min to identify modules from the current yeast gene interaction network with a typical PC (Intel Xeon 2.5 GHz CPU and 512 MB memory). The CPU time of MC2 is affordable (12 h) even when the number of interactions is increased by a factor of 10. MC2 and its results are publicly available on http://theory.med.buffalo.edu/MC2. Keywords: module • network • clique • protein−protein interactio

Engineering Rechargeable Antibacterial Coatings on Stainless Steel for Efficient Inactivation of Pathogenic Bacteria in the Presence of Organic Matter

Worldwide, around 600 million people are affected by foodborne illnesses each year which highlights the importance of food safety. It is important to ensure the cleanliness of the working surfaces in food processing facilities. Stainless steel is widely used in the food industry as a food contact surface. Endowing stainless steel with a potent rechargeable antibacterial function offers the prospect of a reusable and clean surface. In this study, a “clickable” coating for stainless steel was developed. Quaternized azido-hydantoin (C1), quaternary ammonium compound (C2), and azido-hydantoin (C3) were bonded to stainless steel primed with the clickable coating to yield three samples: SSMC1, SSMC2, and SSMC3, respectively. The coating was stable during the chlorination process which was used to convert the immobilized C1 and C3 to their N-chloramine counterparts (SSMC1-Cl and SSMC3-Cl, respectively). It was shown that SSMC1-Cl had the best antibacterial activity with 100% reduction of E. coli and S. aureus after 1 and 2 h of contact, respectively. SSMC1-Cl also showed the best performance in high protein medium (HPM) against bacteria, demonstrating 100% and 99.9% bacterial reduction against E. coli and S. aureus, respectively, after 3 h of contact. After five cycles of chlorination–dechlorination, SSMC1-Cl sustained a kill efficiency of 100% for both E. coli and S. aureus within 2 h of contact. This result reveals that SSMC1-Cl has the ability to maintain its antibacterial activity after repetitive cycles, which emphasizes its rechargeable nature. Altogether, this study presents an effective quaternized N-chloramine-based biocidal coating on stainless steel (SSMC1-Cl) that is rechargeable, durable, and effective against Gram-positive and Gram-negative bacteria

Ultrasensitive Nanofiber Biosensor: Rapid <i>In Situ</i> Chromatic Detection of Bacteria for Healthcare Innovation

Rapid detection of bacterial presence in skin wounds is crucial to prevent the transition from acute to chronic wounds and the onset of systemic infections. Current methods for detecting infections, particularly at low concentrations (5 CFU/cm2), often require complex technologies and direct sampling, which can be invasive and time-consuming. Addressing this gap, we introduce a colorimetric nanofibrous biosensor enabling real-time in situ monitoring of bacterial concentrations in wounds. This biosensor employs a colorimetric hemicyanine dye (HCy) probe, which changes color in response to bacterial lipase, a common secretion in infected wounds. To enhance the biosensor’s sensitivity, we incorporated two key materials science strategies: aligning the nanofibers to promote efficient bacterial attachment and localization and integrating Tween 80, a surfactant, within the nanofiber matrix. This combination of physical and chemical cues results in a notable increase in lipase activity. The cross-aligned core–shell nanofibers, embedded with Tween 80 and HCy, demonstrate an immediate and distinct color change when exposed to as low as 3.0 × 104 CFU/cm2 of common pathogens such as Pseudomonas aeruginosa and MRSA. Significantly, the presence of Tween 80 amplifies the colorimetric response, making visual detection more straightforward and four times more pronounced. Our nanobiosensor design facilitates the detection of low-concentration bacterial infections in situ without the need to remove wound dressings. This advancement marks a significant step forward in real-time wound monitoring, offering a practical tool for the early detection of clinical bacterial infections

The power (percent) of various procedures at <i>α</i> = 0.05 based on 50,000 replicates.

<p>Note: <i>T</i> is the test statistic [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124337#pone.0124337.ref005" target="_blank">5</a>].</p><p>The power (percent) of various procedures at <i>α</i> = 0.05 based on 50,000 replicates.</p

Statistic and p-value for comparing VA for different genetic types of RP.

<p>Statistic and p-value for comparing VA for different genetic types of RP.</p

Computational Studies on an Aminomethylation Precursor: (Xantphos)Pd(CH₂NBn₂)⁺

(Xantphos)­Pd­(CH₂NBn₂)⁺ is an important precursor for aminomethylation reactions. In this study, density functional theory is used to clarify the structure of the complex and the mechanism of these types of reactions. The complex can be described as a mixture of square-planar nitrogen-coordinated aminomethyl–Pd­(II) and triangular iminium-coordinated Pd(0). Frontier molecular orbital analysis favors the latter. The mechanisms of selected aminomethylation reactions are investigated by density functional theory calculations. The computational results reveal that the Xantphos ligand aids in forming iminium-coordinated palladium complexes, promotes the reductive elimination step of aminomethylation, and can stabilize Pd(0) species

Emulating the Logic of Monoterpenoid Alkaloid Biogenesis to Access a Skeletally Diverse Chemical Library

We have developed a synthetic strategy that mimics the diversity-generating power of monoterpenoid indole alkaloid biosynthesis. Our general approach goes beyond diversification of a single natural product-like substructure and enables production of a highly diverse collection of small molecules. The reaction sequence begins with rapid and highly modular assembly of the tetracyclic indoloquinolizidine core, which can be chemoselectively processed into several additional skeletally diverse structural frameworks. The general utility of this approach was demonstrated by parallel synthesis of two representative chemical libraries containing 847 compounds with favorable physicochemical properties to enable its subsequent broad pharmacological evaluation

Emulating the Logic of Monoterpenoid Alkaloid Biogenesis to Access a Skeletally Diverse Chemical Library

We have developed a synthetic strategy that mimics the diversity-generating power of monoterpenoid indole alkaloid biosynthesis. Our general approach goes beyond diversification of a single natural product-like substructure and enables production of a highly diverse collection of small molecules. The reaction sequence begins with rapid and highly modular assembly of the tetracyclic indoloquinolizidine core, which can be chemoselectively processed into several additional skeletally diverse structural frameworks. The general utility of this approach was demonstrated by parallel synthesis of two representative chemical libraries containing 847 compounds with favorable physicochemical properties to enable its subsequent broad pharmacological evaluation