Gender-specific changes in energy metabolism and protein degradation as major pathways affected in livers of mice treated with ibuprofen.

Ibuprofen, an inhibitor of prostanoid biosynthesis, is a common pharmacological agent used for the management of pain, inflammation and fever. However, the chronic use of ibuprofen at high doses is associated with increased risk for cardiovascular, renal, gastrointestinal and liver injuries. The underlying mechanisms of ibuprofen-mediated effects on liver remain unclear. To determine the mechanisms and signaling pathways affected by ibuprofen (100 mg/kg/day for seven days), we performed proteomic profiling of male mice liver with quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) using ten-plex tandem mass tag (TMT) labeling. More than 300 proteins were significantly altered between the control and ibuprofen-treated groups. The data suggests that several major pathways including (1) energy metabolism, (2) protein degradation, (3) fatty acid metabolism and (4) antioxidant system are altered in livers from ibuprofen treated mice. Independent validation of protein changes in energy metabolism and the antioxidant system was carried out by Western blotting and showed sex-related differences. Proteasome and immunoproteasome activity/expression assays showed ibuprofen induced gender-specific proteasome and immunoproteasome dysfunction in liver. The study observed multifactorial gender-specific ibuprofen-mediated effects on mice liver and suggests that males and females are affected differently by ibuprofen

Proteomes of Lactobacillus delbrueckii subsp. bulgaricus LBB.B5 Incubated in Milk at Optimal and Low Temperatures.

We identified the proteins synthesized by Lactobacillus delbrueckii subsp. bulgaricus strain LBB.B5 in laboratory culture medium (MRS) at 37°C and milk at 37 and 4°C. Cell-associated proteins were measured by gel-free, shotgun proteomics using high-performance liquid chromatography coupled with tandem mass spectrophotometry. A total of 635 proteins were recovered from all cultures, among which 72 proteins were milk associated (unique or significantly more abundant in milk). LBB.B5 responded to milk by increasing the production of proteins required for purine biosynthesis, carbohydrate metabolism (LacZ and ManM), energy metabolism (TpiA, PgK, Eno, SdhA, and GapN), amino acid synthesis (MetE, CysK, LBU0412, and AspC) and transport (GlnM and GlnP), and stress response (Trx, MsrA, MecA, and SmpB). The requirement for purines was confirmed by the significantly improved cell yields of L. delbrueckii subsp. bulgaricus when incubated in milk supplemented with adenine and guanine. The L. delbrueckii subsp. bulgaricus-expressed proteome in milk changed upon incubation at 4°C for 5 days and included increased levels of 17 proteins, several of which confer functions in stress tolerance (AddB, UvrC, RecA, and DnaJ). However, even with the activation of stress responses in either milk or MRS, L. delbrueckii subsp. bulgaricus did not survive passage through the murine digestive tract. These findings inform efforts to understand how L. delbrueckii subsp. bulgaricus is adapted to the dairy environment and its implications for its health-benefiting properties in the human digestive tract. IMPORTANCELactobacillus delbrueckii subsp. bulgaricus has a long history of use in yogurt production. Although commonly cocultured with Streptococcus salivarius subsp. thermophilus in milk, fundamental knowledge of the adaptive responses of L. delbrueckii subsp. bulgaricus to the dairy environment and the consequences of those responses on the use of L. delbrueckii subsp. bulgaricus as a probiotic remain to be elucidated. In this study, we identified proteins of L. delbrueckii subsp. bulgaricus LBB.B5 that are synthesized in higher quantities in milk at growth-conducive and non-growth-conductive (refrigeration) temperatures compared to laboratory culture medium and further examined whether those L. delbrueckii subsp. bulgaricus cultures were affected differently in their capacity to survive transit through the murine digestive tract. This work provides novel insight into how a major, food-adapted microbe responds to its primary habitat. Such knowledge can be applied to improve starter culture and yogurt production and to elucidate matrix effects on probiotic performance

REVEILLE8 and PSEUDO-REPONSE REGULATOR5 Form a Negative Feedback Loop within the Arabidopsis Circadian Clock

Circadian rhythms provide organisms with an adaptive advantage, allowing them to regulate physiological and developmental events so that they occur at the most appropriate time of day. In plants, as in other eukaryotes, multiple transcriptional feedback loops are central to clock function. In one such feedback loop, the Myb-like transcription factors CCA1 and LHY directly repress expression of the pseudoresponse regulator TOC1 by binding to an evening element (EE) in the TOC1 promoter. Another key regulatory circuit involves CCA1 and LHY and the TOC1 homologs PRR5, PRR7, and PRR9. Purification of EE–binding proteins from plant extracts followed by mass spectrometry led to the identification of RVE8, a homolog of CCA1 and LHY. Similar to these well-known clock genes, expression of RVE8 is circadian-regulated with a dawn phase of expression, and RVE8 binds specifically to the EE. However, whereas cca1 and lhy mutants have short period phenotypes and overexpression of either gene causes arrhythmia, rve8 mutants have long-period and RVE8-OX plants have short-period phenotypes. Light input to the clock is normal in rve8, but temperature compensation (a hallmark of circadian rhythms) is perturbed. RVE8 binds to the promoters of both TOC1 and PRR5 in the subjective afternoon, but surprisingly only PRR5 expression is perturbed by overexpression of RVE8. Together, our data indicate that RVE8 promotes expression of a subset of EE–containing clock genes towards the end of the subjective day and forms a negative feedback loop with PRR5. Thus RVE8 and its homologs CCA1 and LHY function close to the circadian oscillator but act via distinct molecular mechanisms

Gender-specific changes in energy metabolism and protein degradation as major pathways affected in livers of mice treated with ibuprofen.

Ibuprofen, an inhibitor of prostanoid biosynthesis, is a common pharmacological agent used for the management of pain, inflammation and fever. However, the chronic use of ibuprofen at high doses is associated with increased risk for cardiovascular, renal, gastrointestinal and liver injuries. The underlying mechanisms of ibuprofen-mediated effects on liver remain unclear. To determine the mechanisms and signaling pathways affected by ibuprofen (100 mg/kg/day for seven days), we performed proteomic profiling of male mice liver with quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) using ten-plex tandem mass tag (TMT) labeling. More than 300 proteins were significantly altered between the control and ibuprofen-treated groups. The data suggests that several major pathways including (1) energy metabolism, (2) protein degradation, (3) fatty acid metabolism and (4) antioxidant system are altered in livers from ibuprofen treated mice. Independent validation of protein changes in energy metabolism and the antioxidant system was carried out by Western blotting and showed sex-related differences. Proteasome and immunoproteasome activity/expression assays showed ibuprofen induced gender-specific proteasome and immunoproteasome dysfunction in liver. The study observed multifactorial gender-specific ibuprofen-mediated effects on mice liver and suggests that males and females are affected differently by ibuprofen

Optimal processing for proteomic genotyping of single human hairs.

The use of hair evidence for human identification is undergoing considerable improvement through the adoption of proteomic genotyping. Unlike traditional microscopic comparisons, protein sequencing provides quantitative and empirically based estimates for random match probability. Non-synonymous SNPs are translated as single amino acid polymorphisms and result in genetically variant peptides. Using high resolution mass spectrometry, these peptides can be detected in hair shaft proteins and used to infer the genotypes of corresponding SNP alleles. We describe experiments to optimize the proteomic genotyping approach to individual identification from a single human scalp hair 2 cm in length (∼100 μg). This is a necessary step to develop a protocol that will be useful to forensic investigators. To increase peptide yield from hair, and to maximize genetically variant peptide and ancestral information, we examined the conditions for reduction, alkylation, and protein digestion that specifically address the distinctive chemistry of the hair shaft. Results indicate that optimal conditions for proteomic analysis of a single human hair include 6 h of reduction with 100 mM dithiothreitol at room temperature, alkylation with 200 mM iodoacetamide for 45 min, and 6 h of digestion with two 1:50 (enzyme:protein) additions of stabilized trypsin at room temperature, with stirring incorporated into all three steps. Our final conditions using optimized temperatures and incubation times increased the average number of genetically variant peptides from 20 ± 5 to 73 ± 5 (p = 1 × 10-13), excluding intractable hair samples. Random match probabilities reached up to 1 in 620 million from a single hair with a median value of 1 in 1.1 million, compared to a maximum random match probability of 1 in 1380 and a median value of 1 in 24 for the original hair protein extraction method. Ancestral information was also present in the data. While the number of genetically variant peptides detected were equivalent for both European and African subjects, the estimated random match probabilities for inferred genotypes of European subjects were considerably smaller in African reference populations and vice versa, resulting in a difference in likelihood ratios of 6.8 orders of magnitude. This research will assure uniformity in results across different biogeographic backgrounds and enhance the use of novel peptide analysis in forensic science by helping to optimize genetically variant peptide yields and discovery. This work also introduces two algorithms, GVP Finder and GVP Scout, which facilitate searches, calculate random match probabilities, and aid in discovery of genetically variant peptides

Optimal processing for proteomic genotyping of single human hairs.

The use of hair evidence for human identification is undergoing considerable improvement through the adoption of proteomic genotyping. Unlike traditional microscopic comparisons, protein sequencing provides quantitative and empirically based estimates for random match probability. Non-synonymous SNPs are translated as single amino acid polymorphisms and result in genetically variant peptides. Using high resolution mass spectrometry, these peptides can be detected in hair shaft proteins and used to infer the genotypes of corresponding SNP alleles. We describe experiments to optimize the proteomic genotyping approach to individual identification from a single human scalp hair 2 cm in length (∼100 μg). This is a necessary step to develop a protocol that will be useful to forensic investigators. To increase peptide yield from hair, and to maximize genetically variant peptide and ancestral information, we examined the conditions for reduction, alkylation, and protein digestion that specifically address the distinctive chemistry of the hair shaft. Results indicate that optimal conditions for proteomic analysis of a single human hair include 6 h of reduction with 100 mM dithiothreitol at room temperature, alkylation with 200 mM iodoacetamide for 45 min, and 6 h of digestion with two 1:50 (enzyme:protein) additions of stabilized trypsin at room temperature, with stirring incorporated into all three steps. Our final conditions using optimized temperatures and incubation times increased the average number of genetically variant peptides from 20 ± 5 to 73 ± 5 (p = 1 × 10-13), excluding intractable hair samples. Random match probabilities reached up to 1 in 620 million from a single hair with a median value of 1 in 1.1 million, compared to a maximum random match probability of 1 in 1380 and a median value of 1 in 24 for the original hair protein extraction method. Ancestral information was also present in the data. While the number of genetically variant peptides detected were equivalent for both European and African subjects, the estimated random match probabilities for inferred genotypes of European subjects were considerably smaller in African reference populations and vice versa, resulting in a difference in likelihood ratios of 6.8 orders of magnitude. This research will assure uniformity in results across different biogeographic backgrounds and enhance the use of novel peptide analysis in forensic science by helping to optimize genetically variant peptide yields and discovery. This work also introduces two algorithms, GVP Finder and GVP Scout, which facilitate searches, calculate random match probabilities, and aid in discovery of genetically variant peptides

Identification of Endogenous Peptides in Nasal Swab Transport Media used in MALDI-TOF-MS Based COVID-19 Screening.

Mass spectrometry (MS) based diagnostic detection of 2019 novel coronavirus infectious disease (COVID-19) has been postulated to be a useful alternative to classical PCR based diagnostics. These MS based approaches have the potential to be both rapid and sensitive and can be done on-site without requiring a dedicated laboratory or depending on constrained supply chains (i.e., reagents and consumables). Matrix-assisted laser desorption ionization (MALDI)-time-of-flight (TOF) MS has a long and established history of microorganism detection and systemic disease assessment. Previously, we have shown that automated machine learning (ML) enhanced MALDI-TOF-MS screening of nasal swabs can be both sensitive and specific for COVID-19 detection. The underlying molecules responsible for this detection are generally unknown nor are they required for this automated ML platform to detect COVID-19. However, the identification of these molecules is important for understanding both the mechanism of detection and potentially the biology of the underlying infection. Here, we used nanoscale liquid chromatography tandem MS to identify endogenous peptides found in nasal swab saline transport media to identify peptides in the same the mass over charge (m/z) values observed by the MALDI-TOF-MS method. With our peptidomics workflow, we demonstrate that we can identify endogenous peptides and endogenous protease cut sites. Further, we show that SARS-CoV-2 viral peptides were not readily detected and are highly unlikely to be responsible for the accuracy of MALDI based SARS-CoV-2 diagnostics. Further analysis with more samples will be needed to validate our findings, but the methodology proves to be promising

Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.

The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted "gold standard", molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests

Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.

The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted "gold standard", molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests