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Enhancement of glycolysis and glutaminolysis are the two most common modalities associated with metabolic reprogramming in rheumatoid arthritis (RA). This enhancement is concomitant to the upregulation of hexokinase 2 (HK2) and glutaminase 1 (GLS1). Hence, the current study was undertaken to identify potential phytobiological inhibitors against HK2 and GLS1, from Dracaena (Sansevieria) trifasciata, an indigenous ethnomedicinal plant found in Pakistan, using computational analysis. Phytobiologics from Dracaena trifasciata were assessed for their ability to co-inhibit HK2 and GLS1 via molecular docking and molecular dynamics simulations. The results underscored seven phytobiologics with promising binding affinities for both HK2 and GLS1. Molecular dynamics simulations further elucidated that all seven identified phytobiologics inhibited HK2 by forming stable complexes but only five amongst the seven had the potential to form stable complexes with GLS1 in real time, thereby implying the potential of co-inhibition for these five compounds. Compound 28MS exhibited an equally strong binding profile for both HK2 (−8.19 kcal/mol) and GLS1 (−8.99 kcal/mol). Furthermore, it exhibited a similar trend in stability during simulation for both targets. Our results serve as a primer for a more lucid understanding towards co-inhibition of HK2 and GLS1 using multiple computational approaches. The identified phytobiologics should undergo in-vitro and in-vivo validation to corroborate their therapeutic potential in RA.

Rheumatoid arthritis is an autoimmune disorder of complex disease etiology. Currently available serological diagnostic markers lack in terms of sensitivity and specificity and thus additional biomarkers are warranted for early disease diagnosis and management. We aimed to screen and compare serum proteome profiles of rheumatoid arthritis serotypes with healthy controls in the Pakistani population for identification of potential disease biomarkers. Serum samples from rheumatoid arthritis patients and healthy controls were enriched for low abundance proteins using ProteoMinerTM columns. Rheumatoid arthritis patients were assigned to one of the four serotypes based on anti-citrullinated peptide antibodies and rheumatoid factor. Serum protein profiles were analyzed via liquid chromatography-tandem mass spectrometry. The changes in the protein abundances were determined using label-free quantification software ProgenesisQITM followed by pathway analysis. Findings were validated in an independent cohort of patients and healthy controls using an enzyme-linked immunosorbent assay. A total of 213 proteins were identified. Comparative analysis of all groups (false discovery rate < 0.05, >2-fold change, and identified with ≥2 unique peptides) identified ten proteins that were differentially expressed between rheumatoid arthritis serotypes and healthy controls including pregnancy zone protein, selenoprotein P, C4b-binding protein beta chain, apolipoprotein M, N-acetylmuramoyl-L-alanine amidase, catalytic chain, oncoprotein-induced transcript 3 protein, Carboxypeptidase N subunit 2, Apolipoprotein C-I and Apolipoprotein C-III. Pathway analysis predicted inhibition of liver X receptor/retinoid X receptor activation pathway and production of nitric oxide and reactive oxygen species pathway in macrophages in all serotypes. A catalogue of potential serum biomarkers for rheumatoid arthritis were identified. These biomarkers can be further evaluated in larger cohorts from different populations for their diagnostic and prognostic potential.