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Abstract Target identification is an essential part of the drug discovery and development process, and its efficacy plays a crucial role in the success of any given therapy. Although protein target identification research can be challenging, two main approaches can help researchers make significant discoveries: affinity-based pull-down and label-free methods. Affinity-based pull-down methods use small molecules conjugated with tags to selectively isolate target proteins, while label-free methods utilize small molecules in their natural state to identify targets. Target identification strategy selection is essential to the success of any drug discovery process and must be carefully considered when determining how to best pursue a specific project. This paper provides an overview of the current target identification approaches in drug discovery related to experimental biological assays, focusing primarily on affinity-based pull-down and label-free approaches, and discusses their main limitations and advantages.

Machine learning (ML) in toxicological sciences is growing exponentially, which presents unprecedented opportunities and brings up important considerations for using ML in this field. This review discusses supervised, unsupervised, and reinforcement learning and their applications to toxicology. The application of the scientific method is central to the development of a ML model. These steps involve defining the ML problem, constructing the dataset, transforming the data and feature selection, choosing and training a ML model, validation, and prediction. The need for rigorous models is becoming more of a requirement due to the vast number of chemicals and their interaction with biota. Large datasets make this task possible, though selecting databases with overlapping chemical spaces, amongst other things, is an important consideration. Predicting toxicity through machine learning can have significant societal impacts, including enhancements in assessing risks, determining clinical toxicities, evaluating carcinogenic properties, and detecting harmful side effects of medications. We provide a concise overview of the current state of this topic, focusing on the potential benefits and challenges related to the availability of extensive datasets, the methodologies for analyzing these datasets, and the ethical implications involved in applying such models.

The use of small molecules in immunotherapy is a promising and rapidly growing field with the potential to revolutionize the way we treat a wide range of immunological diseases. The success stories of using small molecules in immunotherapy have been highlighted with many drugs, such as checkpoint inhibitors, which have achieved significant success in treating several types of tumors. However, despite this success, the development of small molecules for immunotherapy is still in its infancy, and there are many challenges that remain to be addressed. In a recent study, our laboratory reported the immunostimulatory effect of a small molecule (Compound A) (A). In this paper, we studied a derivative of this molecule (Compound B) (Fig. 1B). and analysed its physiochemical properties, immunological activities, and transcriptome profiling. Compound B exhibited acceptable metabolic stability and no toxicity against PBMCs. We also demonstrated that Compound B was capable of modulating the immune system by inducing pro-inflammatory cytokines and promoting T-cell proliferation. RNAseq results showed that Compound B was able to significantly upregulate genes involved in stimulating the immune response pathways. Our findings suggest that Compound B may serve as a promising therapeutic agent to modulate the immune system. The identification and validation of the molecular targets responsible for its immunological activities are underway.

In the past decade, there has been increasing interest in use of small molecules for immunomodulation. The affinity-based pull-down purification is an essential tool for target identification of small molecules and drug discovery. This study presents our recent efforts to investigate the cellular target(s) of Compound A, a small molecule with demonstrated immunomodulatory properties in human peripheral blood mononuclear cells (PBMCs). While we have previously observed the immunomodulatory activity of Compound A in PBMCs, the specific molecular targets underlying its effects remains elusive. To address this challenge, we synthesized a trifluoromethyl phenyl diazirine (TPD)-bearing trifunctional Probe 1 based on the chemical structure of Compound A, which could be used in a pull-down assay to efficiently bind to putative cellular targets via photoaffinity labelling. In this report, we utilized bovine serum albumin (BSA) as a model protein to establish a proof-of-concept in order to assess the suitability of Probe 1 for binding to an endogenous target. By the successful synthesis of Probe 1 and demonstrating the efficient binding of Probe 1 to BSA, we propose that this method can be used as a tool for further identification of potential protein targets of small molecules in living cells. Our findings provide a valuable starting point for further investigations into the molecular mechanisms underlying the immunomodulatory effects of Compound A.

A significant proportion of babies that are admitted to the neonatal intensive care unit (NICU) suffer from late onset sepsis (LOS). In order to prevent mortality and morbidity, the early detection of LOS is of the utmost importance. Recent works have found that the use of machine learning techniques might help detect LOS at an early stage. Some works have shown that linear methods (i.e., logistic regression) display a superior performance when predicting LOS. Nevertheless, as research on this topic is still in an early phase, it has not been ruled out that non-linear machine learning (ML) techniques can improve the predictive performance. Moreover, few studies have assessed the effect of parameters other than heart rate variability (HRV). Therefore, the current study investigates the effect of non-linear methods and assesses whether other vital parameters such as respiratory rate, perfusion index, and oxygen saturation could be of added value when predicting LOS. In contrast with the findings in the literature, it was found that non-linear methods showed a superior performance compared with linear models. In particular, it was found that random forest performed best (AUROC: 0.973), 24% better than logistic regression (AUROC: 0.782). Nevertheless, logistic regression was found to perform similarly to some non-linear models when trained with a short training window. Furthermore, when also taking training time into account, K-Nearest Neighbors was found to be the most beneficial (AUROC: 0.950). In line with the literature, we found that training the models on HRV features yielded the best results. Lastly, the results revealed that non-linear methods demonstrated a superior performance compared with linear methods when adding respiratory features to the HRV feature set, which ensured the greatest improvement in terms of AUROC score.

Contamination of barley by deoxynivalenol (DON), a mycotoxin produced by Fusarium graminearum, causes considerable financial loss to the grain and malting industries. In this study, two atmospheric cold plasma (ACP) reactors were used to produce plasma-activated water (PAW) bubbles. The potential of PAW bubbles for the steeping of naturally infected barley (NIB) during the malting process was investigated. The PAW bubbles produced by treating water for 30 min using a bubble spark discharge (BSD) at low temperature resulted in the greatest concentration of oxygen-nitrogen reactive species (RONS). This treatment resulted in 57.3% DON degradation compared with 36.9% in the control sample; however, the same treatment reduced germination significantly (p < 0.05). Direct BSD ACP treatment for 20 min at low temperature and indirect treatment for 30 min increased the percentage of germinated rootlets of the seedlings compared with the control. Considering both the DON reduction and germination improvement of barley seeds, continuous jet ACP treatment for 30 min performed better than the other treatments used in this study. At higher temperature of PAW bubbles, the concentration of RONS was significantly (p < 0.05) reduced. Based on quantitative polymerase chain reaction (qPCR) analysis and fungal culture tests, the PAW bubble treatment did not significantly reduce infection of NIB. Nonetheless, this study provides useful information for the malting industry for PAW treatment optimization and its use in barley steeping for DON reduction and germination improvement.

This review provides a practical guide to myeloperoxidase (MPO) and presents to the reader the diversity of its presence in biology. The review provides a historical background, from peroxidase activity to the discovery of MPO, to its role in disease and drug development. MPO is discussed in terms of its necessity, as specific individuals lack MPO expression. An underlying theme presented throughout brings up the question of the benefit and burden of MPO activity. Enzyme structure is discussed, including accurate masses and glycosylation sites. The catalytic cycle of MPO and its corresponding pathways are presented, with a discussion of the importance of the redox couples of the different states of MPO. Cell lines expressing MPO are discussed and practically summarized for the reader, and locations of MPO (primary and secondary) are provided. Useful methods of MPO detection are discussed, and how these can be used for studying disease processes are implied through the presentation of MPO as a biomarker. The presence of MPO in neutrophil extracellular traps is presented, and the activators of the former are provided. Lastly, the transition from drug metabolism to a target for drug development is where the review concludes.

Isoniazid (INH) is one of the first-line anti-tuberculosis drugs. Its effect on oxidative stress, however, is unknown. Here we used a model of oxidative stress by employing glucose/glucose oxidase (GOx), which (based on the availability of glucose and oxygen) is known to produce H2O2. This reaction induces oxidative stress culminating in necrotic cell death in HL-60 cells (a human promyelocytic leukemia cell line). The changes in protein levels have been quantified using global proteome expression changes through stable isotope labeling by amino acids in cell culture (SILAC) followed by LC–MS/MS analysis. A total of 1459 and 1712 proteins were identified in forward and reverse experiments, respectively. However, only 390 proteins were reproducibly identified in both samples. These 390 proteins were taken into account for further analysis which has been described in “Cytoprotective effect of isoniazid against H2O2 derived injury in HL-60 cells” [1].

Recent studies have failed to demonstrate a causal cardioprotective effect of HDL cholesterol levels, shifting focus to the functional aspects of HDL. Phospholipid transfer protein (PLTP) is an HDL-associated protein involved in reverse cholesterol transport. This study sought to determine the genetic and nongenetic predictors of plasma PLTP activity (PLTPa), and separately, to determine whether PLTPa predicted carotid artery disease (CAAD). PLTPa was measured in 1,115 European ancestry participants from a case-control study of CAAD. A multivariate logistic regression model was used to elucidate the relationship between PLTPa and CAAD. Separately, a stepwise linear regression determined the nongenetic clinical and laboratory characteristics that best predicted PLTPa. A final stepwise regression considering both nongenetic and genetic variables identified the combination of covariates that explained maximal PLTPa variance. PLTPa was significantly associated with CAAD (7.90 × 10−9), with a 9% decrease in odds of CAAD per 1 unit increase in PLTPa (odds ratio = 0.91). Triglyceride levels (P = 0.0042), diabetes (P = 7.28 × 10−5), paraoxonase 1 (PON1) activity (P = 0.019), statin use (P = 0.026), PLTP SNP rs4810479 (P = 6.38 × 10−7), and PCIF1 SNP rs181914932 (P = 0.041) were all significantly associated with PLTPa. PLTPa is significantly inversely correlated with CAAD. Furthermore, we report a novel association between PLTPa and PON1 activity, a known predictor of CAAD.

Oxidative stress plays a role in the onset and progression of a number of diseases, such as Alzheimer’s disease, diabetes and cancer, as well as ageing. Oxidative stress is caused by an increased production of reactive oxygen species and reduced antioxidant activity, resulting in the oxidation of glutathione. The ratio of reduced to oxidised glutathione is often used as a marker of the redox state in the cell. Whereas a variety of methods have been developed to measure glutathione in blood samples, methods to measure glutathione in cultured cells are scarce. Here we present a protocol to measure glutathione levels in cultured human and yeast cells using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC–MS/MS).

Phospholipid transfer protein activity (PLTPa) is associated with insulin levels and has been implicated in atherosclerotic disease in both mice and humans. Variation at the PLTP structural locus on chromosome 20 explains some, but not all, heritable variation in PLTPa. In order to detect quantitative trait loci (QTLs) elsewhere in the genome that affect PLTPa, we performed both oligogenic and single QTL linkage analysis on four large families (n = 227 with phenotype, n = 330 with genotype, n = 462 total), ascertained for familial combined hyperlipidemia. We detected evidence of linkage between PLTPa and chromosome 19p (lod = 3.2) for a single family and chromosome 2q (lod = 2.8) for all families. Inclusion of additional marker and exome sequence data in the analysis refined the linkage signal on chromosome 19 and implicated coding variation in LASS4, a gene regulated by leptin that is involved in ceramide synthesis. Association between PLTPa and LASS4 variation was replicated in the other three families (P= 0.02), adjusting for pedigree structure. To our knowledge, this is the first example for which exome data was used in families to identify a complex QTL that is not the structural locus.

Phospholipid transfer protein (PLTP) belongs to the lipid transfer/lipopolysaccharide-binding protein gene family. Expression of PLTP has been implicated in the development of atherosclerosis. We evaluated the effects of PLTP region tagging single nucleotide polymorphisms (SNPs) on the prediction of both carotid artery disease (CAAD) and PLTP activity. CAAD effects were evaluated in 442 Caucasian male subjects with severe CAAD and 497 vascular disease-free controls. SNP prediction of PLTP transfer activity was evaluated in both a subsample of 87 subjects enriched for an allele of interest and in a confirmation sample of 210 Caucasian males and females. Hemoglobin A1c or insulin level predicted 11–14% of age- and sex-adjusted PLTP activity. PLTP SNPs that predicted ∼11–30% of adjusted PLTP activity variance were identified in the two cohorts. For rs6065904, the allele that was associated with CAAD was also associated with elevated PLTP activity in both cohorts. SNPs associated with PLTP activity also predicted variation in LDL-cholesterol and LDL-B level only in the replication cohort. These results demonstrate that PLTP activity is strongly influenced by PLTP region polymorphisms and metabolic factors.

Small, dense LDLs and hypertriglyceridemia, two highly correlated and genetically influenced risk factors, are known to predict for risk of coronary heart disease. The objective of this study was to perform a whole-genome scan for linkage to LDL size and triglyceride (TG) levels in 26 kindreds with familial hypertriglyceridemia (FHTG). LDL size was estimated using gradient gel electrophoresis, and genotyping was performed for 355 autosomal markers with an average heterozygosity of 76% and an average spacing of 10.2 centimorgans (cMs). Using variance components linkage analysis, one possible linkage was found for LDL size [logarithm of odds (LOD) = 2.1] on chromosome 6, peak at 140 cM distal to marker F13A1 (closest marker D6S2436). With adjustment for TG and/or HDL cholesterol, the LOD scores were reduced, but remained in exactly the same location. For TG, LOD scores of 2.56 and 2.44 were observed at two locations on chromosome 15, with peaks at 29 and 61 cM distal to marker D15S822 (closest markers D15S643 and D15S211, respectively). These peaks were retained with adjustment for LDL size and/or HDL cholesterol.These findings, if confirmed, suggest that LDL particle size and plasma TG levels could be caused by two different genetic loci in FHTG.

To achieve a more sustainable global food production, a shift from animal to plant protein based food is necessary. At the same time, these plant proteins are preferentially derived from side-streams of industrial processes. Wheat bran and germ represent two major side-streams of the wheat milling industry, and contain aqueous-phase soluble proteins with a well-balanced amino acid composition. To successfully use wheat bran and germ proteins in novel plant-based liquid and semi-solid foods, they need to (i) be rendered extractable and (ii) contribute functionally to stabilizing the food system. Prior heat treatment and the occurrence of intact cell walls are important barriers in this regard. Several strategies have been applied to overcome these issues, including physical processing and (bio)chemical modification. We here present a comprehensive, critical overview of the aqueous-phase extraction of protein from (modified) wheat bran and germ. Moreover, we discuss the functionality of the extracted protein, specifically in the context of liquid (foam- and emulsion-type) and semi-solid (gel-type) food applications. In each section, we identify important knowledge gaps and highlight several future prospects that could further increase the application potential of wheat bran and germ proteins in the food industry. [ABSTRACT FROM AUTHOR]

Recommendations for laboratories to report incidental findings from genomic tests have stimulated interest in such results. In order to investigate the criteria and processes for assigning the pathogenicity of specific variants and to estimate the frequency of such incidental findings in patients of European and African ancestry, we classified potentially actionable pathogenic single-nucleotide variants (SNVs) in all 4300 European- and 2203 African-ancestry participants sequenced by the NHLBI Exome Sequencing Project (ESP). We considered 112 gene-disease pairs selected by an expert panel as associated with medically actionable genetic disorders that may be undiagnosed in adults. The resulting classifications were compared to classifications from other clinical and research genetic testing laboratories, as well as with in silico pathogenicity scores. Among European-ancestry participants, 30 of 4300 (0.7%) had a pathogenic SNV and six (0.1%) had a disruptive variant that was expected to be pathogenic, whereas 52 (1.2%) had likely pathogenic SNVs. For African-ancestry participants, six of 2203 (0.3%) had a pathogenic SNV and six (0.3%) had an expected pathogenic disruptive variant, whereas 13 (0.6%) had likely pathogenic SNVs. Genomic Evolutionary Rate Profiling mammalian conservation score and the Combined Annotation Dependent Depletion summary score of conservation, substitution, regulation, and other evidence were compared across pathogenicity assignments and appear to have utility in variant classification. This work provides a refined estimate of the burden of adult onset, medically actionable incidental findings expected from exome sequencing, highlights challenges in variant classification, and demonstrates the need for a better curated variant interpretation knowledge base.