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Protein fragmentation is a critical component of top-down proteomics, enabling gene-specific protein identification and full proteoform characterization. The factors that influence protein fragmentation include precursor charge, structure, and primary sequence, which have been explored extensively for collision-induced dissociation (CID). Recently, noticeable differences in CID-based fragmentation were reported for native versus denatured proteins, motivating the need for scoring metrics that are tailored specifically to native top-down mass spectrometry (nTDMS). To this end, position and intensity were tracked for 10,252 fragment ions produced by higher-energy collisional dissociation (HCD) of 159 native monomers and 70 complexes. We used published structural data to explore the relationship between fragmentation and protein topology and revealed that fragmentation events occur at a large range of relative residue solvent accessibility. Additionally, our analysis found that fragment ions at sites with an N-terminal aspartic acid or a C-terminal proline make up on average 40 and 27%, respectively, of the total matched fragment ion intensity in nTDMS. Percent intensity contributed by each amino acid was determined and converted into weights to (1) update the previously published C-score and (2) construct a native Fragmentation Propensity Score. Both scoring systems showed an improvement in protein identification or characterization in comparison to traditional methods and overall increased confidence in results with fewer matched fragment ions but with high probability nTDMS fragmentation patterns. Given the rise of nTDMS as a tool for structural mass spectrometry, we forward these scoring metrics as new methods to enhance analysis of nTDMS data.

Rhoifolin (ROF) is a main effective component in Citrus grandis 'Tomentosa'. ROF has a potential anti-inflammatory activity, but its specific effects and mechanisms have not been studied. This study investigated the anti-inflammatory activity of ROF and searched for its possible molecular mechanisms. A mouse model of acute inflammation was induced by lipopolysaccharide, and the effects of ROF on pathological damages of the lung and liver were observed. Carrageenan-induced paw edema rat model was used to evaluate the effect of ROF on the volume of swelling paw. In LPS-induced RAW264.7 macrophages, the expression levels of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α were measured using ELISA. Real-time PCR was used to measure the mRNA levels of iNOS and CCL2. Western blot was used to detect the activation of IκBα and IKKβ in NF-κB signaling pathways. The results showed that ROF accelerated the recoveries of liver and lung tissue damages in acute inflammation mice and inhibited carrageenan-induced paw edema in rats; in addition, ROF significantly suppressed the secretion of TNF-α, IL-1β, and IL-6 in the serum of rats and mouse model. In LPS-induced RAW264.7 cells, 100 μmol/L ROF enhanced cell viability and suppressed the production of TNF-α, IL-6, and IL-1β significantly. ROF also decreased the mRNA expression of iNOS and CCL2 and inhibited IκBα and IKKβ phosphorylation. In summary, ROF had a potential therapeutic value for inflammation. Our research provided experimental basis for the further development of ROF as an anti-inflammatory drug and for clarifying the anti-inflammatory substance basis of Citrus grandis 'Tomentosa'. Graphical Abstract.

RATIONALE Methicillin-resistant Staphylococcus aureus (MRSA) is one of major clinical pathogens responsible for both hospital- and community-acquired infections worldwide. A delay in targeted antibiotic treatment contributes to longer hospitalization stay, higher costs, and increasing in-hospital mortality. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been integrated into the routine workflow for microbial identification over the past decade, and it has also shown promising functions in the detection of bacterial resistance. Therefore, we describe a rapid MALDI-TOF MS-based methodology for MRSA screening with machine-learning algorithms. METHODS A total of 452 clinical S. aureus isolates were included in this study, of which 194 were MRSA and 258 were methicillin-sensitive S. aureus (MSSA). The mass-to-charge ratio (m/z) features from MRSA and MSSA strains were binned and selected through Lasso regression. These features were then used to train a non-linear support vector machine (SVM) with radial basis function (RBF) kernels to evaluate the discrimination performance. The classifiers' accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were evaluated and compared with those from the random forest (RF) model. RESULTS A total of 2601 unique spectral peaks of all isolates were identified and 38 m/z features were selected for the classifying model. The AUCs of the non-linear RBF-SVM model and the RF model were 0.89 and 0.87, respectively, and the accuracy ranged between 0.86 (RBF-SVM) and 0.82 (RF). CONCLUSIONS Our study demonstrates that MALDI-TOF MS coupled with machine-learning algorithms could be used to develop a rapid and easy-to-use method to discriminate MRSA from MSSA. Considering that this method is easy to implement in routine microbiology laboratories, it suggests a cost-effective and time-efficient alternative to conventional resistance detection in the future to improve clinical treatment.

Cancer cells reprogram their metabolism to support growth and to mitigate cellular stressors. The serine synthesis pathway has been identified as a metabolic pathway frequently altered in cancers and there has been considerable interest in developing pharmacological agents to target this pathway. Here, we report a series of indole amides that inhibit human 3-phosphoglycerate dehydrogenase (PHGDH), the enzyme that catalyzes the first committed step of the serine synthesis pathway. Using X-ray crystallography, we show that the indole amides bind the NAD(+) pocket of PHGDH. Through structure-based optimization we were able to develop compounds with low nanomolar affinities for PHGDH in an enzymatic IC(50) assay. In cellular assays, the most potent compounds inhibited de novo serine synthesis with low micromolar to sub-micromolar activities and these compounds successfully abrogated the proliferation of cancer cells in serine free media. The indole amide series reported here represent an important improvement over previously published PHGDH inhibitors as they are markedly more potent and their mechanism of action is better defined.

Cancer cells can adapt to nutrient poor conditions by rewiring their metabolism and using alternate fuel sources. Identifying these adaptive metabolic pathways may provide novel targets for cancer therapy. Here, we identify a subset of non-small cell lung cancer (NSCLC) cell lines that survive in the absence of glucose by internalizing and metabolizing extracellular protein via macropinocytosis. Macropinocytosis is increased in these glucose independent cells, and is regulated by phosphoinositide 3-kinase (PI3K) activation of Rac-Pak signaling. Furthermore, inhibition of Rac-dependent macropinocytosis blocks glucose-independent proliferation. We find that degradation of internalized protein produces amino acids, including alanine, which generates TCA cycle and glycolytic intermediates in the absence of glucose. In this process, the conversion of alanine to pyruvate by alanine transaminase 2 (ALT2) is critical for survival during glucose starvation. Collectively, Rac driven macropinocytosis of extracellular protein is an adaptive metabolic pathway used by a subset of lung cancers to survive states of glucose deprivation, and may serve as a potential drug target for cancer therapy.