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1. Sulfur isotope analysis of cysteine and methionine via preparatory liquid chromatography and elemental analyzer isotope ratio mass spectrometry

   https://doi.org/10.1002/rcm.9007  

   Phillips, Alexandra_A; Wu, Fenfang; Sessions, Alex_L (January 2021, Rapid Communications in Mass Spectrometry)

   RationaleSulfur isotope analysis of organic sulfur‐containing molecules has previously been hindered by challenging preparatory chemistry and analytical requirements for large sample sizes. The natural‐abundance sulfur isotopic compositions of the sulfur‐containing amino acids, cysteine and methionine, have therefore not yet been investigated despite potential utility in biomedicine, ecology, oceanography, biogeochemistry, and other fields. MethodsCysteine and methionine were subjected to hot acid hydrolysis followed by quantitative oxidation in performic acid to yield cysteic acid and methionine sulfone. These stable, oxidized products were then separated by reversed‐phase high‐performance liquid chromatography (HPLC) and verified via offline liquid chromatography/mass spectrometry (LC/MS). The sulfur isotope ratios (δ³⁴S values) of purified analytes were then measured via combustion elemental analyzer coupled to isotope ratio mass spectrometry (EA/IRMS). The EA was equipped with a temperature‐ramped chromatographic column and programmable helium carrier flow rates. ResultsOn‐column focusing of SO₂in the EA/IRMS system, combined with reduced He carrier flow during elution, greatly improved sensitivity, allowing precise (0.1–0.3‰ 1 s.d.) δ³⁴S measurements of 1 to 10 μg sulfur. We validated that our method for purification of cysteine and methionine was negligibly fractionating using amino acid and protein standards. Proof‐of‐concept measurements of fish muscle tissue and bacteria demonstrated differences up to 4‰ between the δ³⁴S values of cysteine and methionine that can be connected to biosynthetic pathways. ConclusionsWe have developed a sensitive, precise method for measuring the natural‐abundance sulfur isotopic compositions of cysteine and methionine isolated from biological samples. This capability opens up diverse applications of sulfur isotopes in amino acids and proteins, from use as a tracer in organisms and the environment, to fundamental aspects of metabolism and biosynthesis. 

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2. Analytical chemistry students’ explanatory statements in the context of their corresponding lecture

   https://doi.org/10.1039/d0rp00063a  

   Wang, Ying; Lewis, Scott E. (September 2020, Chemistry Education Research and Practice)

   null (Ed.)

   Conceptually understanding chemistry requires the ability to transition among representational levels to use an understanding of submicroscopic entities and properties to explain macroscopic phenomena. Past literature describes student struggles with these transitions but provides limited information about upper-level post-secondary chemistry students’ abilities to transition among levels. This group is of particular interest as they are engaging in potentially their final training before entering a career as professional chemists, thus if students are likely to develop this skill during their formal education it should be manifest among this group. This study characterized analytical chemistry students’ responses to open-ended assessments on acid–base titrations and thin-layer chromatography for the use of sub-microscopic entities or properties to explain these macroscopic phenomena. Further, to understand whether explanatory statements were an expectation inherent in the instructional context of the setting, the analytical chemistry instructor's lectures on acid–base titrations and thin-layer chromatography were analyzed with the same framework. The analysis found that students seldom invoked explanatory statements within their responses and that congruence between lectures and responses to assessment was primarily limited to the use of macroscopic, descriptive terms. Despite the fact that the lecture in class regularly invoked explanatory statements in one context, this did not translate to student use of explanatory statements. To further test the hypothesis that analytical chemistry students struggle with explanatory statements, a follow-on study was also conducted among a second cohort of students reviewing their responses when specifically prompted to use sub-microscopic entities to explain a macroscopic phenomenon. The results suggest that fewer than half of the students showed proficiency on generating explanatory statements when explicitly prompted to do so. Instructional implications to promote explanatory statements are proposed in the discussion. 

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3. Phenylpropanoid-enriched broccoli seedling extract can reduce inflammatory markers and pain behavior

   https://doi.org/10.1186/s12967-023-04777-1  

   Gurgul, Aleksandra A.; Najjar, Yahya; Chee, Ana; An, Howard; Che, Chun-Tao; Park, Thomas J.; Warpeha, Katherine M. (December 2023, Journal of Translational Medicine)

   Abstract BackgroundPain is a worldwide problem requiring an effective, affordable, non-addictive therapy. Using the edible plant broccoli, a growth protocol was developed to induce a concentrated combinatorial of potential anti-inflammatories in seedlings. MethodsA growth method was utilized to produce a phenylpropanoid-rich broccoli sprout extract, referred to as Original Extract (OE). OE was concentrated and then resuspended for study of the effects on inflammation events. A rabbit disc model of inflammation and degeneration, and, a mouse model of pain behavior were used for in vivo and in vitro tests. To address aspects of mammalian metabolic processing, the OE was treated with the S9 liver microsome fraction derived from mouse, for use in a mouse in vivo study. Analytical chemistry was performed to identify major chemical species. Continuous variables were analyzed with a number of methods including ANOVA, and two-tailedttests, as appropriate. ResultsIn a rabbit spine (disc) injury model, inflammatory markers were reduced, and levels of regenerative markers were increased as a result of OE treatment, both in vivo and in vitro. In a mouse pain behavioral model, after treatment with S9 liver microsome fraction, the resultant extract significantly reduced early and late pain behavior in response to a pain stimulus. The OE itself reduced pain behavior in the mouse pain model, but did not achieve the level of significance observed for S9-treated extract. Analytical chemistry undertaken on the extract constituents revealed identities of the chemical species in OE, and how S9 liver microsome fraction treatment altered species identities and proportions. ConclusionsIn vitro and in vivo results indicate that the OE, and S9-treated OE broccoli extracts are worthwhile materials to develop a non-opiate inflammation and pain-reducing treatment. 

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4. Heterogeneous entity representation for medicinal synergy prediction

   https://doi.org/10.1093/bioinformatics/btae750  

   Wu, Jiawei; Wen, Jun; Yan, Mingyuan; Dong, Anqi; Gao, Shuai; Wang, Ren; Chen, Can (December 2024, Bioinformatics)

   Lu, Zhiyong (Ed.)

   Abstract MotivationForecasting the synergistic effects of drug combinations facilitates drug discovery and development, especially regarding cancer therapeutics. While numerous computational methods have emerged, most of them fall short in fully modeling the relationships among clinical entities including drugs, cell lines, and diseases, which hampers their ability to generalize to drug combinations involving unseen drugs. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy. ResultsWe present a novel deep hypergraph learning method named Heterogeneous Entity Representation for MEdicinal Synergy (HERMES) prediction to predict the synergistic effects of anti-cancer drugs. Heterogeneous data sources, including drug chemical structures, gene expression profiles, and disease clinical semantics, are integrated into hypergraph neural networks equipped with a gated residual mechanism to enhance high-order relationship modeling. HERMES demonstrates state-of-the-art performance on two benchmark datasets, significantly outperforming existing methods in predicting the synergistic effects of drug combinations, particularly in cases involving unseen drugs. Availability and implementationThe source code is available at https://github.com/Christina327/HERMES. 

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5. ORTEGA v1.0: an open-source Python package for context-aware interaction analysis using movement data

   https://doi.org/10.1186/s40462-024-00460-2  

   Su, Rongxiang; Liu, Yifei; Dodge, Somayeh (December 2024, Movement Ecology)

   Abstract BackgroundInteraction analysis via movement in space and time contributes to understanding social relationships among individuals and their dynamics in ecological systems. While there is an exciting growth in research in computational methods for interaction analysis using movement data, there remain challenges regarding reproducibility and replicability of the existing approaches. The current movement interaction analysis tools are often less accessible or tested for broader use in ecological research. To address these challenges, this paper presents ORTEGA, an Object-oRiented TimE-Geographic Analytical tool, as an open-source Python package for analyzing potential interactions between pairs of moving entities based on the observation of their movement. ORTEGA is developed based on one of the newly emerged time-geographic approaches for quantifying space-time interaction patterns among animals. A case study is presented to demonstrate and evaluate the functionalities of ORTEGA in tracing dynamic interaction patterns in animal movement data. Besides making the analytical code and data freely available to the community, the developed package also offers an extension of the existing theoretical development of ORTEGA for incorporating a context-aware ability to inform interaction analysis. ORTEGA contributes two significant capabilities: (1) the functions to identify potential interactions (e.g., encounters, concurrent interactions, delayed interactions) from movement data of two or more entities using a time-geographic-based approach; and (2) the capacity to compute attributes of potential interaction events including start time, end time, interaction duration, and difference in movement parameters such as speed and moving direction, and also contextualize the identified potential interaction events. 

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