Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r²) ≤ −0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia.

Background: Tumor glycolysis is a target for cancer chemotherapy. Methylglyoxal (MG) is a reactive metabolite formed mainly as a by-product in anaerobic glycolysis, metabolized by glyoxalase 1 (Glo1) of the glyoxalase system. We investigated the role of MG and Glo1 in cancer chemotherapy related in multidrug resistance (MDR).Methods: Human Glo1 was overexpressed in HEK293 cells and the effect on anticancer drug potency, drug-induced increase in MG and mechanism of cytotoxicity characterized. Drug-induced increased MG and the mechanisms driving it were investigated and the proteomic response to MG-induced cytotoxicity explored by high mass resolution proteomics of cytoplasmic and other subcellular protein extracts. Glo1 expression data of 1,040 human tumor cell lines and 7,489 tumors were examined for functional correlates and impact of cancer patient survival.Results: Overexpression of Glo1 decreased cytotoxicity of antitumor drugs, impairing antiproliferative activity of alkylating agents, topoisomerase inhibitors, antitubulins, and antimetabolites. Antitumor drugs increased MG to cytotoxic levels which contributed to the cytotoxic, antiproliferative mechanism of action, consistent with Glo1-mediated MDR. This was linked to off-target effects of drugs on glycolysis and was potentiated in hypoxia. MG activated the intrinsic pathway of apoptosis, with decrease of mitochondrial and spliceosomal proteins. Spliceosomal proteins were targets of MG modification. Spliceosomal gene expression correlated positively with Glo1 in human tumor cell lines and tumors. In clinical chemotherapy of breast cancer, increased expression of Glo1 was associated with decreased patient survival, with hazard ratio (HR) = 1.82 (logrank p < 0.001, n = 683) where upper quartile survival of patients was decreased by 64% with high Glo1 expression.Conclusions: We conclude that MG-mediated cytotoxicity contributes to the cancer chemotherapeutic response and targets the spliceosome. High expression of Glo1 contributes to multidrug resistance by shielding the spliceosome from MG modification and decreasing survival in the chemotherapy of breast cancer. Adjunct chemotherapy with Glo1 inhibitor may improve treatment outcomes.

Autism spectrum disorder (ASD) is an umbrella term that encompasses several disabling neurodevelopmental conditions. These conditions are characterized by impaired manifestation in social and communication skills with repetitive and restrictive behaviors or interests. Thus far, there are no approved biomarkers for ASD screening and diagnosis; also, the current diagnosis depends heavily on a physician’s assessment and family’s awareness of ASD symptoms. Identifying blood proteomic biomarkers and performing deep blood proteome profiling could highlight common underlying dysfunctions between cases of ASD, given its heterogeneous nature, thus laying the foundation for large-scale blood-based biomarker discovery studies. This study measured the expression of 1196 serum proteins using proximity extension assay (PEA) technology. The screened serum samples included ASD cases (n = 91) and healthy controls (n = 30) between 6 and 15 years of age. Our findings revealed 251 differentially expressed proteins between ASD and healthy controls, of which 237 proteins were significantly upregulated and 14 proteins were significantly downregulated. Machine learning analysis identified 15 proteins that could be biomarkers for ASD with an area under the curve (AUC) = 0.876 using support vector machine (SVM). Gene Ontology (GO) analysis of the top differentially expressed proteins (TopDE) and weighted gene co-expression analysis (WGCNA) revealed dysregulation of SNARE vesicular transport and ErbB pathways in ASD cases. Furthermore, correlation analysis showed that proteins from those pathways correlate with ASD severity. Further validation and verification of the identified biomarkers and pathways are warranted.

SUMMARY: The global pandemic of COVID-19 disease caused by infection with the SARS-CoV-2 coronavirus, has produced an urgent requirement and search for improved treatments whilst effective vaccines are developed. A strategy for improved drug therapy is to increase levels of endogenous reactive metabolites for selective toxicity to SARS-CoV-2 by preferential damage to the viral proteome. Key reactive metabolites producing major quantitative damage to the proteome in physiological systems are: reactive oxygen species (ROS) and the reactive glycating agent methylglyoxal (MG); cysteine residues and arginine residues are their most susceptible targets, respectively. From sequenced-based prediction of the SARS-CoV-2 proteome, we found 0.8-fold enrichment or depletion of cysteine residues in functional domains of the viral proteome; whereas there was a 4.6-fold enrichment of arginine residues, suggesting SARS-CoV-2 is resistant to oxidative agents and sensitive to MG. For arginine residues of the SARS-CoV-2 predicted to be in functional domains, we examined which are activated towards modification by MG – residues with predicted or expected low pK _a by neighbouring group in interactions. We found 25 such arginine residues, including 2 in the spike protein and 10 in the nucleoprotein. These sites were partially conserved in related coronaviridae : SARS-COV and MERS. Finally, we identified drugs which increase cellular MG concentration to virucidal levels: antitumor drugs with historical antiviral activity, doxorubicin and paclitaxel. Our findings provide evidence of potential vulnerability of SARS-CoV-2 to inactivation by MG and a scientific rationale for repurposing of doxorubicin and paclitaxel for treatment of COVID-19 disease, providing efficacy and adequate therapeutic index may be established.

Background: Alzheimer’s disease (AD) is the most common form of dementia, and its prevalence continues to increase with absence of available interventions. Drug repurposing accelerates the drug discovery process as therapeutics with known safety and toxicology profiles are considered. Although the blood‐brain barrier (BBB) prevents uptake of most pharmaceuticals, our laboratory previously showed that tolfenamic acid (TA), a fenamate non‐steroidal anti‐inflammatory drug (NSAID) with known anti‐cancer effects, can cross the BBB and slow the progression of AD (Zawia et al, 2018).Method: In the present study, we test 2‐(2‐Cyanophenylthio) benzoic acid (CBA) and Niflumic acid (NA), small molecule analogs of TA that showed better safety and efficacy at targeting neurodegenerative pathways in vitro, and a similar mechanism of action to TA. We aimed to examine the effects of the analogs on in mice using proteomic and transcriptomic analyses.Single dose study of TA, CBA, and NA was carried out in C57BL/6 mice following oral administration at 100 mg/kg. Differential expression of genes and proteomic biomarkers in mice cortices were analyzed between each treatment group and a corresponding control group.Results: Analysis of the number of perturbed genes revealed that the drugs demonstrate limited off‐target effects. Bioinformatic analyses revealed that the therapeutics targeted cancer and neurodegenerative pathways including autophagy, inflammation, and neuronal trafficking. CBA and NA shared common downstream targets and pathways suggesting that they may have a similar mode of action.Conclusion: We found a set of differentially expressed genes that have been linked to processes involved in AD pathogenesis such as the production and clearance of neurotoxic Aβ peptides, tau phosphorylation, regulation of oxidative stress, and inflammatory processes. These results suggest that the drugs may influence pathways related to the pathogenesis of AD and support the potential of drug repurposing of fenamate NSAIDs such as TA, and its analogs CBA and NA, as a strategy for cancer and neurodegenerative disease therapy. Further research is needed to confirm the effect of the analogs on AD biomarkers and their efficacy as therapeutics for AD.