Profiling Approach Research Articles

Effect of prawn fed with Catharanthus roseus ethanolic extract incorporated diet in the protein profile approaches in gene expressions related and immune characteristics in giant freshwater prawn (Macrobrachium rosenbergii)

Effect of prawn fed with Catharanthus roseus ethanolic extract incorporated diet in the protein profile approaches in gene expressions related and immune characteristics in giant freshwater prawn (Macrobrachium rosenbergii)

Targeted quantification and untargeted exploration of furan and derivatives in infant food by headspace extraction-gas chromatography-Q Exactive Orbitrap mass spectrometry

The aim of the present study was to assess the performance and complementarity of methods capable of both quantifying furan, 2-Methylfuran (2-MF) and 3-Methylfuran (3-MF) in infant foods, but also to comprehensively explore other furan derivatives. It is more particularly a question of validating and comparing the couplings of the two headspace extraction methods most used for the analysis of furan compounds − Headspace Solid Phase Microextraction (HS-SPME) and Static HeadSpace (SHS) – with gas chromatography hyphenated to a high-resolution mass detector (Q Exactive-Orbitrap MS) which allows both targeted quantification and suspect screening. Firstly, the accuracy profile approach was implemented to assess, validate and compare HS-SPME- and SHS-GC-Q Exactive-Orbitrap MS for the quantification of furan in two model infant foods, apple puree and first infant formula. SHS-GC-Q Exactive-Orbitrap MS, showed better accuracy (uncertainty < 17.2 % vs 22.5 % for HS-SPME GC-Q Exactive-Orbitrap MS) and better sensitivity (LOQ < 2.8 vs LOQ < 4.0 µg/kg) over a broader validation range (2–100 µg/kg vs 5–100 µg/kg in apple puree). Secondly, SHS-GC-Q Exactive-Orbitrap MS was assessed and validated by accuracy profile for the quantification of 2-MF and 3-MF, with performance close to those for furan except for 3-MF in apple puree. Thirdly, SHS-GC-Q Exactive-Orbitrap MS was used to quantify the levels of these compounds in 20 commercial samples (n = 3) belonging to the four main categories of infant food (infant formulae, fruit purees, infant cereals, vegetable/fish baby meals). Furan was quantified in 75 % of the samples, with maximum levels in the vegetable/fish-based infant foods (up to 127 µg/kg) while 2-MF and 3-MF were quantified in 45 % and 15 % of products respectively, with maximum levels of 14.1 µg/kg in follow-on formula 3rd age and 9.2 µg/kg in apple puree. Finally, SHS- and HS-SPME-GC-Q Exactive-Orbitrap MS data of the 20 infant products were processed in suspect screening mode using Compound DiscovererTM software. Coupling with HS-SPME, it made it possible to identify 13 additional furan derivatives, i.e. 5 more than with SHS. The relevance and safety status of the compounds identified are discussed.

Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase.

Despite the success of AlphaFold methods in predicting single protein structures, these methods showed intrinsic limitations in the characterization of multiple functional conformations of allosteric proteins. The recent NMR-based structural determination of the unbound ABL kinase in the active state and discovery of the inactive low-populated functional conformations that are unique for ABL kinase present an ideal challenge for the AlphaFold2 approaches. In the current study, we employ several adaptations of the AlphaFold2 methodology to predict protein conformational ensembles and allosteric states of the ABL kinase including randomized alanine sequence scanning combined with the multiple sequence alignment subsampling proposed in this study. We show that the proposed new AlphaFold2 adaptation combined with local frustration profiling of conformational states enables accurate prediction of the protein kinase structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AlphaFold2 predictions and detection of hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AlphaFold2. The results of this study uncovered previously unappreciated fundamental connections between local frustration profiles of the functional allosteric states and the ability of AlphaFold2 methods to predict protein structural ensembles of the active and inactive states. This study showed that integration of the randomized sequence scanning adaptation of AlphaFold2 with a robust landscape-based analysis allows for interpretable atomistic predictions and characterization of protein conformational ensembles, providing a physical basis for the successes and limitations of current AlphaFold2 methods in detecting functional allosteric states that play a significant role in protein kinase regulation.

Exploring flavor perception through metabolite profiling and sensory approaches during starter kimchi fermentation

Starter cultures can enhance sensory properties during the starter kimchi fermentation. However, while many studies have primarily focused on physicochemical properties, the impact of starters on flavor perception remains unclear. The aim of this study was to assess the impact of starter culture inoculation, specifically Companilactobacillus allii WiKim39 and Lactococcus lactis WiKim0124, on aroma and flavor perception through a comparison of metabolite profiling and electronic tongue and nose data. Notably, 19 and 11 compounds were identified using UHPLC-Q-Orbitrap-MS and HPLC, respectively. Principal component analysis revealed that variations in fermentation metabolites and sensory properties were significantly categorized based on the particular starter culture employed during the ripening process. Moreover, the correlation analysis between the 19 key fermentative metabolites and e-tongue measurements in each starter kimchi revealed significant differences in sourness (AHS), bitterness (SCS), and comprehensive taste (CPS and PKS) sensors. The e-nose system identified four major volatile compounds contributing to the sweetness and off-flavor-related aroma profile of the starter kimchi. These findings highlight the significant impact that starter culture has on taste and flavor development, serving as a valuable guide in predicting sensory properties during starter kimchi fermentation.

Structural Optimization of Scarfing Machine with Acceleration Profile and Multi-Objective Genetic Algorithm Approach

Scarfing is a type of flame treatment used to improve the quality of metal generated during steelmaking. It employs the principles of gas cutting to remove impurities and defects. Due to the high-temperature conditions and the need for uniform metal treatment, mechanical scarfing performed via a frame is preferred over manual hand scarfing. To achieve stable mechanical scarfing, a properly designed frame is essential. Generally, while using more material can create stable equipment, it also increases costs. Therefore, this study proposed a design method that selects an acceleration profile to minimize the shock on the frame during scarfing equipment operation while using a multi-objective genetic algorithm to minimize weight and maximize rigidity. Because modifying existing scarfing equipment based on the optimization results would incur additional costs and time, pre-optimizing through simulation before equipment fabrication is crucial. Optimization was achieved via the dimensional optimization of the existing frame equipment. As a result, the weight of each part and the deformation decreased by an average of 17.05 kg and 3.93%, respectively.

Efficient aerodynamic optimization of turbine blade profiles: an integrated approach with novel HDSPSO algorithm

PurposeThis paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.Design/methodology/approachAn efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability.FindingsMathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions.Originality/valueThese findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications.

Machine Learning Approaches to Predict the Selectivity of Compounds against HDAC1 and HDAC6

The design of compounds selectively binding to specific isoforms of histone deacetylases (HDACs) is ongoing research to prevent adverse side effects. Two of the most studied isoforms are HDAC1 and HDAC6 which are important targets in various disease conditions. Here, various machine learning (ML) approaches were built and tested to predict the bioactivity and selectivity towards specific isoforms. The selectivities of compounds were precited using two different approaches: selectivity profiling and selectivity window approaches. The bioactivity models of HDAC1 and HDAC6 were used to determine the selectivities of compounds in the selectivity profiling approach. In the selectivity window approach, models were developed by directly training on the bioactivity differences of tested compounds against HDAC1 and HDAC6. In this study, firstly all available classification and regression ML algorithms in Python package were tested to find suitable algorithms. Five ML algorithms were selected based on their performances and algorithm differences. These models were compared to each other by using traditional evaluation metrics. Then, a consensus approach of the selected ML models was employed to compare the selectivity window and selectivity profiling approaches regarding their ability to distinguish HDAC1- and HDAC6-selective compounds from others. Here, the performances were also tested against an external set and it was seen that the selectivity window approach was performing slightly better in categorizing selective compounds than the selectivity profiling approach. The approaches presented in this study could be an important step to utilize for screening molecular libraries to discover selective inhibitors for targets.

Comprehensive characterization of bioactive peptides in rice bran protein hydrolysates: A multi‐platform approach for metabolomic profiling, identification, and molecular docking analysis

AbstractBackground and ObjectivesTo optimize the utilization of rice by‐products, the rice bran protein hydrolysate (RBPH) was subjected to a process of separation and purification using ultrafiltration and reversed‐phase high‐performance liquid chromatography (RP‐HPLC). Subsequently, the identification of peptide sequences was carried out using mass spectrometer hybrid quadrupole‐time‐of‐flight and nontargeted metabolomic profiling done using UHPLC‐QTOF‐IMS profiling to identify antioxidant and antihypertensive amino acids.FindingsThree novel bioactive peptides, namely, STCCK, FMKSK, and KICILVFTLTTC, were identified within RBPH through enzymatic digestion using alcalase, pepsin, and trypsin. These peptides showed significant antioxidant activity and angiotensin‐converting enzyme (ACE) inhibitory activity. Molecular docking analyses elucidated various interaction mechanisms between these peptides and the ACE receptor protein, including hydrogen bonding and hydrophobic interactions. This suggested that the peptides effectively suppress ACE by forming hydrogen bonds with the active pockets of human ACE with a high affinity.ConclusionIn summary, peptides derived from rice bran protein exhibited distinguished antihypertensive and antioxidant properties, underscoring their potential for valuable utilization in addressing utilization of rice by‐products.Significance and NoveltyThe study advances scientific knowledge and offers practical solutions for health improvement and sustainable agriculture.

The 5'-terminal stem-loop RNA element of SARS-CoV-2 features highly dynamic structural elements that are sensitive to differences in cellular pH.

We present the nuclear magnetic resonance spectroscopy (NMR) solution structure of the 5'-terminal stem loop 5_SL1 (SL1) of the SARS-CoV-2 genome. SL1 contains two A-form helical elements and two regions with non-canonical structure, namely an apical pyrimidine-rich loop and an asymmetric internal loop with one and two nucleotides at the 5'- and 3'-terminal part of the sequence, respectively. The conformational ensemble representing the averaged solution structure of SL1 was validated using NMR residual dipolar coupling (RDC) and small-angle X-ray scattering (SAXS) data. We show that the internal loop is the major binding site for fragments of low molecular weight. This internal loop of SL1 can be stabilized by an A12-C28 interaction that promotes the transient formation of an A+•C base pair. As a consequence, the pKa of the internal loop adenosine A12 is shifted to 5.8, compared to a pKa of 3.63 of free adenosine. Furthermore, applying a recently developed pH-differential mutational profiling (PD-MaP) approach, we not only recapitulated our NMR findings of SL1 but also unveiled multiple sites potentially sensitive to pH across the 5'-UTR of SARS-CoV-2.

Individual and team profiling to support theory of mind in artificial social intelligence

We describe an approach aimed at helping artificial intelligence develop theory of mind of their human teammates to support team interactions. We show how this can be supported through the provision of quantifiable, machine-readable, a priori information about the human team members to an agent. We first show how our profiling approach can capture individual team member characteristic profiles that can be constructed from sparse data and provided to agents to support the development of artificial theory of mind. We then show how it captures features of team composition that may influence team performance. We document this through an experiment examining factors influencing the performance of ad-hoc teams executing a complex team coordination task when paired with an artificial social intelligence (ASI) teammate. We report the relationship between the individual and team characteristics and measures related to task performance and self-reported perceptions of the ASI. The results show that individual and emergent team profiles were able to characterize features of the team that predicted behavior and explain differences in perceptions of ASI. Further, the features of these profiles may interact differently when teams work with human versus ASI advisors. Most strikingly, our analyses showed that ASI advisors had a strong positive impact on low potential teams such that they improved the performance of those teams across mission outcome measures. We discuss these findings in the context of developing intelligent technologies capable of social cognition and engage in collaborative behaviors that improve team effectiveness.

Development of a serum miRNA panel for detection of early stage non-small cell lung cancer.

3058 Background: Lung cancer remains the leading cause of cancer deaths, largely due to late-stage diagnosis. Early detection significantly improves survival rates, but current methods like chest X-rays and low-dose CT (LDCT) scans face limitations in sensitivity and specificity. MicroRNAs (miRNAs) are small non-coding RNAs whose dysregulation has been widely observed in different stages of cancer. Evidence suggests that circulating miRNAs could be promising biomarkers for early diagnosis of lung cancer. In this study, we developed and validated a 15-miRNA panel as a blood-based minimally invasive test for early detection of non-small cell lung cancer (NSCLC) using the proprietary multiplex quantitative real-time PCR (qPCR). Methods: 332 serum samples (182 Stage I&amp;II NSCLC, 150 healthy controls) were divided into training (n=266) and validation (n=66) cohorts. Differentially expressed serum miRNAs were screened with Miseq sequencing followed by qPCR validation. The multi-miR panel was developed with a logistic regression model and validated using an independent cohort. Receiver operating characteristic curves were constructed to evaluate the diagnostic accuracy of the panel. A stem-loop quadruplex qPCR assay was invented for concurrent detection of 4 miRs in a single reaction. Results: 15 differentially expressed miRNAs were selected for logistic model construction based on their Ct values (Ct&lt;35) and high diagnostic accuracy of stage I&amp;II NSCLC (AUC&gt;0.7). The 15-miRNA panel model achieved 88.4% sensitivity, 81.7% specificity (AUC = 0.912) in the training cohort (Table), and was validated with 94.4% sensitivity, 73.3% specificity (AUC = 0.892) in the validation cohort. Quantification of 15 miRs and 1 internal control was performed in 4 reactions tubes via quadruplex qPCR assay. Conclusions: The serum 15-miR panel developed and validated in this retrospective study exhibited robust performance for detection of early stage NSCLC. The panel demonstrates potential diagnostic applications and clinical utility to be implemented together with LDCT in current lung cancer screening program as a blood biomarker to reduce false positive results. In addition, the proposed stem-loop quadruplex qPCR system offers an efficient and promising approach for miRNA profiling in future clinical applications. [Table: see text]

Systematic biases in reference-based plasma cell-free DNA fragmentomic profiling

Plasma cell-free DNA (cfDNA) fragmentation patterns are emerging directions in cancer liquid biopsy with high translational significance. Conventionally, the cfDNA sequencing reads are aligned to a reference genome to extract their fragmentomic features. In this study, through cfDNA fragmentomics profiling using different reference genomes on the same datasets in parallel, we report systematic biases in such conventional reference-based approaches. The biases in cfDNA fragmentomic features vary among races in a sample-dependent manner and therefore might adversely affect the performances of cancer diagnosis assays across multiple clinical centers. In addition, to circumvent the analytical biases, we develop Freefly, a reference-free approach for cfDNA fragmentomics profiling. Freefly runs ∼60-fold faster than the conventional reference-based approach while generating highly consistent results. Moreover, cfDNA fragmentomic features reported by Freefly can be directly used for cancer diagnosis. Hence, Freefly possesses translational merit toward the rapid and unbiased measurement of cfDNA fragmentomics.

Real-world costs and outcomes associated with biomarker testing by comprehensive genomic profiling (CGP) and non-CGP approaches among patients with metastatic non-small cell lung cancer (mNSCLC).

e20598 Background: Guideline-recommended molecular testing is essential for identifying appropriate targeted therapies (Rx) for treatment of mNSCLC patients (pts). Biomarker testing can be performed by single gene tests, small NGS panels (e.g., &lt; 50 genes), or CGP approaches. There is little evidence on the rate of biomarker testing in the real world setting and the impact of different approaches on Rx utilization and cost of care. This study examined real-world utilization of biomarker testing among mNSCLC pts and outcomes with CGP and non-CGP testing. Methods: De-identified administrative claims data from the Optum Labs Data Warehouse were analyzed to identify newly diagnosed adult mNSCLC pts from 1/2018 to 8/2021; the date of the first claim indicating metastasis was the index date. Continuous enrollment in a commercial (COM) or Medicare Advantage (MA) plan with medical and pharmacy benefits for 12 mo prior to (baseline), and ≥6 mo post index date (follow-up) was required; pts with &lt; 6 mo follow-up due to death were included. Initiation of a line of therapy (LOT1) during follow-up was required. We categorized pts based on receipt and type of biomarker testing prior to LOT1: CGP ( &gt; 50 gene panel), non-CGP (5-50 gene panels or single gene testing), or no testing. Differences in receipt of targeted Rx, overall survival (OS), and total overall per patient per month (PPPM) costs during LOT1 were examined with multivariable regression analyses. Results: 9,945 mNSCLC pts (1,970 COM, 7,975 MA) were identified: 5,484 with no testing, 2,215 with CGP, and 2,246 with non-CGP testing prior to LOT1. Biomarker testing rates prior to LOT1 were low (45%) but increased during the study period from 42% to 48% (p &lt;0.01). Testing rates were higher for the COM vs MA population (49% vs 44%, p &lt;0.01). A higher percent of pts with CGP received targeted Rx compared to the non-CGP and no testing group (17% vs 11% and 5% respectively, p&lt; 0.01); after adjustment, CGP pts were still more likely to receive targeted Rx. Compared to the no testing group, OS was more favorable for the CGP [HR 0.8, 95% CI 0.8-0.9] and non-CGP [HR 0.9, 95% CI 0.8-0.9] groups. There was no significant difference in PPPM costs between tested groups: CGP [CR 1.2, 95%CI 1.1-1.2] vs non-CGP [CR 1.1, 95%CI 1.1-1.2]. Conclusions: Rates of biomarker testing among mNSCLC pts are far from optimal despite well-established guideline recommendations and insurance coverage for testing. There was evidence of improved intermediate outcomes (receipt of targeted Rx) with CGP compared to non-CGP or no testing. In addition, OS was improved for tested pts compared to untested. Interventions to help improve biomarker testing are needed. Given the potential benefits of CGP testing (including assessment of biomarkers that cannot be evaluated using small panels), increasing CGP testing may improve outcomes.

Classification of HER2 status across multiple cancers using epigenomic profiles from a novel liquid biopsy assay.

3047 Background: The efficacy of trastuzumab deruxtecan in multiple cancers led to a recent FDA granting of priority review for the treatment of adults with HER2-positive solid tumors. This underscores the critical need for reliable assessment of HER2 expression across cancers. The current standard using IHC/ISH for HER2 scoring is fraught with challenges including scoring discordance and tumor heterogeneity. Here we describe the ability to classify HER2 status from 1 ml of plasma, using epigenomic signatures from a novel multi-analyte liquid biopsy (LBx) platform, offering a minimally invasive approach for patient (pt) selection across cancers. Methods: We selected 179 samples from 172 pts with advanced breast (BC), gastro-esophageal (GEA) and ovarian (OV) cancers who had associated HER2 status scored from tissue-based IHC/ISH according to ASCO/CAP guidelines (table). Samples were taken at baseline or at progression and those with detectable cell free DNA, as assessed by iChorCNA, were profiled for genome-wide epigenomic signals across histone modifications associated with active enhancers, promoters and DNA methylation. A regularized regression model developed to classify HER2 status in BC cell lines and was refined and validated for HER2 status prediction for each pt cohort (HER2+ = 3+, 2+/ISH+; HER2- = 2+/ISH-, 1+, 0). The BC cell-line derived HER2 classifier was refined to incorporate GEA and OV cancer specific features before being applied to those samples. Performance was assessed via AUC in a leave-one-out cross-validation schema. We also evaluated HER2 status at progression in a subset of pts with benchmarked HER2 IHC to assess dynamic changes in receptor status by LBx. Results: The epigenomic HER2 classifier was applied to all samples with ctDNA detectable by ichorCNA (90 of 179; 50%). HER2 classification of BC pts by epigenomic liquid biopsy was concordant with standard tissue-based IHC for 64/72 (89%) BC samples (AUC 0.9, table). HER2 classifier predictions for all longitudinally collected samples were concordant with IHC-based HER2 status, including the one patient whose status switched from HER2+ to HER2- at progression. Accurate classification of 11/14 (79%, GEA) and 4/4 (100%, OV) pts was achieved using the indication-refined HER2 classifier. Conclusions: We demonstrate proof of concept for a HER2 classification approach using comprehensive epigenomic signals from 1 ml of plasma that could be applied across multiple cancers. With further development, our genome-wide profiling approach could alleviate clinical constraints associated with multiple tissue-based HER2 scoring assays (IHC/ISH) and enable longitudinal monitoring of HER2 status on therapy. [Table: see text]

Molecular profiling in ABC: Is complementary testing with tissue and plasma necessary and clinically relevant?

e12566 Background: This retrospective analysis explores the concordance between molecular analyses of tissue and plasma amongst Advanced Breast Cancer (ABC) patients and investigates the utility of simultaneous profiling for enhanced clinical insights. Methods: A total of 187 (177 Cancertrack paired and 10 Guardant 360) patient samples with availability of both tissue and ctDNA specimens, were subjected to comparative molecular profiling. Median age was 52 years (Range 23 – 82 years), 174 (93%) patients had advanced breast cancer and 177 (95%) patients had received prior treatments. We assigned tier 1 status to predictive biomarkers with FDA approved or guideline recommended therapies in breast cancer. Tier 2 status was assigned to alterations for which therapies were approved in different tumor type. Tier 3 had either clinical evidence in small studies or had clinical trial inclusion criteria. Results: Paired tissue-ctDNA profiling showed 139/187 (74%) samples had at least one concordant molecular alteration. Simultaneous tissue and ctDNA profiling led to the opportunity to offer Tier 1 recommended therapies in 113/187 (60%) patients. Amongst these, 71/113 (63%) exhibited concordance in tissue and circulating free DNA (cfDNA). ctDNA missed Tier 1 alterations in 14 patients which were detected by tissue biopsy. On the contrary tissue biopsy failed to detect Tier 1 alterations in 18 patients which were identified by ctDNA only. When Tier 2 and 3 alterations were considered, ctDNA identified additional alterations compared to tissue in 29 patients (37 variants), whereas tissue identified additional alterations in 20 patients (24 variants). Complementary tissue and ctDNA profiling showed alterations with potential to impact clinical management (Tier1/2/3) in 82% patients. When including only Tier 1 and 2 alterations, simultaneous profiling approach gives an opportunity to offer personalized therapy in 68% patients. The Tier1 ctDNA only alterations were most frequent in ESR1 gene. In fact, a total of a total of 29 ESR1 mutations were detected in these patients, of which 10 mutations were detected in both tissue and liquid. In 3 patients’ tissues were positive for ESR1 mutations but the corresponding ctDNA was negative. Similarly, ctDNA detected 16 ESR1 variants which were negative in the tissue sample. The most common ESR1 variants were D538G and Y537S. Conclusions: The simultaneous profiling of tissue and ctDNA enhanced overall clinical utility of molecular analysis in advanced breast cancer by identifying predictive biomarkers. The integration of data from both sources allowed for comprehensive understanding of the molecular heterogeneity in breast cancer. It underscores the potential of simultaneous profiling to guide treatment decisions and improve patient outcomes. By detecting acquired resistance ESR1 mutations more effectively, ctDNA serves as a valuable tool for monitoring as well.

Proteomic Profiling of Antimalarial Plasmodione Using 3-Benz(o)ylmenadione Affinity-Based Probes.

Understanding the mechanisms of drug action in malarial parasites is crucial for the development of new drugs to combat infection and to counteract drug resistance. Proteomics is a widely used approach to study host-pathogen systems and to identify drug protein targets. Plasmodione is an antiplasmodial early-lead drug exerting potent activities against young asexual and sexual blood stages in vitro with low toxicity to host cells. To elucidate its molecular mechanisms, an affinity-based protein profiling (AfBPP) approach was applied to yeast and P. falciparum proteomes. New (pro-) AfBPP probes based on the 3-benz(o)yl-6-fluoro-menadione scaffold were synthesized. With optimized conditions of both photoaffinity labeling and click reaction steps, the AfBPP protocol was then applied to a yeast proteome, yielding 11 putative drug-protein targets. Among these, we found four proteins associated with oxidoreductase activities, the hypothesized type of targets for plasmodione and its metabolites, and other proteins associated with the mitochondria. In Plasmodium parasites, the MS analysis revealed 44 potential plasmodione targets that need to be validated in further studies. Finally, the localization of a 3-benzyl-6-fluoromenadione AfBPP probe was studied in the subcellular structures of the parasite at the trophozoite stage.

Sustainable Agrochemical Exploitation of Ligurian Aromatic Plant Ecotypes.

The characterization of local improved varieties as well as the reduction of synthetic chemical fertilizers are sustainable approaches in the vision of a new precision Farming. Aim of our study was to improve the geographical characterization of local ecotypes and to identify peculiar features of new crops in terms of bioactive compounds. NMR and LC-MS metabolite profiling approaches followed by multivariate data analysis were applied to characterize local rosemary and garlic ecotypes. With the aim of applying for a protected designation of origin, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify representative sensory quality indicators for Vessalico garlic and rosemary "Eretto Liguria" local ecotypes, Variable Influence on Projections (VIP) values of OPLS-DA indicated six metabolites as quality indicators for Vessalico garlic and sixteen metabolites as quality indicators for rosemary "Eretto Liguria". Finally, to discover and utilize new ecotypes in a sustainable way, Vessalico garlic extracts antiviral activity, previously evaluated against Tomato brown rugose fruit virus (ToBRFV), a Tobamovirus affecting tomato crops, was extended to Pepino mosaic virus (PepMV) with positive results.

Incidental Germline Findings During Comprehensive Genomic Profiling of Pancreatic and Colorectal Cancer: Single-center, Molecular Tumor Board Experience.

Abstract Multidisciplinary molecular tumor boards (MTB) are already well established in many comprehensive cancer centers and play an important role in the individual treatment planning for cancer patients. Comprehensive genomic profiling of tumor tissue based on next-generation sequencing is currently performed for diagnostic and mainly predictive testing. If somatic genomic variants are identified, which are suspected to be pathogenic germline variants (PGVs), MTB propose genetic counseling and germline DNA testing. Commonly used comprehensive genomic profiling approaches of tumor tissue do not include a matched germline DNA control. Therefore, the detection of PGVs could be only predicted based on the content of tumor cells (CTC) in selected tumor area (%) and variant allele frequency score (%). For conclusion, the role of a medical geneticist is essential in these cases. The overall prevalence of PGVs in patients with pancreatic ductal adenocarcinoma (PDAC) and colorectal cancer (CRC) is approximately 10%. In this single-center study, we present 37 patients with PDAC and 48 patients with CRC who were presented at MTB and tested using the large combined DNA/RNA sequencing panel. Content of tumor cells and variant allele frequency scores were evaluated in all tested patients. In case of suspicion of PGV and no previous genetic testing based on the standard guidelines, genetic counseling was recommended regardless of age, sex, and family history. In the PDAC subgroup, five patients were recommended by MTB for genetic counseling based on suspicious genetic findings. Based on a medical geneticist’s decision, germline DNA sequencing was performed in four of these cases, and all of them tested positive for PGV in the following genes: ATM, ATM, BRCA1, and BRCA2. In the CRC subgroup, no PGV was confirmed in the two patients genetically tested based on the MTB recommendations. Furthermore, we present data from our center’s registry of patients with PDAC and CRC who underwent genetic counseling and germline DNA testing based on the standard screening criteria. Our data confirm that comprehensive genomic profiling of tumor tissue can identify patients with hereditary forms of PDAC, who could remain unidentified by standard screening for hereditary forms of cancer.

Clients' experiences of empathy in genetic counseling for hereditary breast and ovarian cancer: A qualitative study in Japan.

Empathy is a significant element in genetic counseling for building relationships with the clients and addressing their issues. However, there are few reports on the experiences of the clients about their perceived empathy in genetic counseling. Cancer genetic counseling needs have been rapidly evolving with the expansion of clinical comprehensive genomic profiling and genetic diagnosis approaches for hereditary cancers. Therefore, this study aimed to reveal empathy perceptions of the clients during cancer genetic counseling. Semi-structured interviews were conducted, and a grounded theory approach was used for data analysis. A total of 13 participants were recruited from organizations for patients with cancer, among whom 11 were patients with hereditary breast and ovarian cancer (HBOC) and two were relatives of patients with HBOC. Data analysis was organized into five categories related to experiences with empathy: (i) prior context to perceive empathy (ii) understanding and consideration, (iii) bedside manner, and (iv) impacted area of perceived empathy; and (v) no empathy. This study highlights the fact that empathy experiences of the clients differ depending on the situation and state of mind. Taken together, this study provides new insights on how to deliver empathic care.

Rapid GC-MS based metabolic profiling of Capsicum annuum L. seeds at different phases of germination

Seed germination is the most critical stage of the life cycle of a plant. Metabolic regulation is significant during the course of seed germination for the establishment of seedlings. Upon imbibition, the dry and fully developed seeds undergo the release of various organic molecules, such as low molecular weight carbonyl compounds in the form of gases and volatiles, as well as water-soluble organic components like enzymes and polysaccharides. Volatile organic compounds may impart both positive and negative influences on seed germination. A metabolite profiling approach based on gas chromatography−mass spectrometry (GC-MS) was used to investigate time-dependent metabolic changes during the germination of Capsicum annuum L. cv. ‘Bullet’. This study aimed to identify bioactive compounds from the methanolic extract of dry, imbibed, germinating seeds and young seedlings of the ‘Bullet’ cultivar of C. annuum L. by GC-MS. A total of 56 distinct categories of compounds were identified in dry seeds, while fully absorbed seeds contained 53 compounds. In the germinating seed, 52 compounds were identified. With regard to immature seedlings, a total of 28 compounds were identified. The analysis revealed that each stage has a unique bio-constituent; only five compounds were detected in all extracts.