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In the current study, APOB ( rs1052031) genotype-guided proteomic analysis was performed in a cohort of Pakistani population. A total of 700 study subjects, including Coronary Artery Disease (CAD) patients (n = 480) and healthy individuals (n = 220) as a control group were included in the study. Genotyping was carried out by using tetra primer-amplification refractory mutation system-based polymerase chain reaction (T-ARMS-PCR) whereas mass spectrometry (Orbitrap MS) was used for label free quantification of serum samples. Genotypic frequency of GG genotype was found to be 90.1%, while 6.4% was for GA genotype and 3.5% was for AA genotypes in CAD patients. In the control group, 87.2% healthy subjects were found to have GG genotype, 11.8% had GA genotype, and 0.9% were with AA genotypes. Significant ( p  = 0.007) difference was observed between genotypic frequencies in the patients and the control group. The rare allele AA was found to be strongly associated with the CAD [OR: 4 (1.9–16.7)], as compared to the control group in recessive genetic model ( p  = 0.04). Using label free proteomics, altered expression of 60 significant proteins was observed. Enrichment analysis of these protein showed higher number of up-regulated pathways, including phosphatidylcholine-sterol O -acyltransferase activator activity, cholesterol transfer activity, and sterol transfer activity in AA genotype of rs562338 (G>A) as compared to the wild type GG genotype. This study provides a deeper insight into CAD pathobiology with reference to proteogenomics, and proving this approach as a good platform for identifying the novel proteins and signaling pathways in relation to cardiovascular diseases.

Intellectual disability is a heterogeneous disorder, diagnosed using intelligence quotient (IQ) score criteria. Currently, no specific clinical test is available to diagnose the disease and its subgroups due to inadequate understanding of the pathophysiology. Therefore, current study was designed to explore the molecular mechanisms involved in disease perturbation, and to identify potential biomarkers for disease diagnosis and prognosis. A total of 250 participants were enrolled in this study, including 200 intellectually disabled (ID) subjects from the subgroups (mild, moderate, and severe) with age and gender matched healthy controls (n = 50). Initially, IQ testing score and biochemical profile of each subject was generated, followed by label-free quantitative proteomics of subgroups of IQ and healthy control group through nano-LC/MS- mass spectrometry. A total of 310 proteins were identified, among them198 proteins were common among all groups. Statistical analysis (ANOVA) of the subgroups of ID showed 142 differentially expressed proteins, in comparison to healthy control group. From these, 120 proteins were found to be common among all subgroups. The remaining 22 proteins were categorized as exclusive proteins found only in disease subgroups. Furthermore, the hierarchical cluster analysis (HCL) of common significant proteins was also performed, followed by PANTHER protein classification and GO functional enrichment analysis. Results provides that the datasets of differentially expressed proteins, belong to the categories of immune / defense proteins, transfer carrier proteins, apolipoproteins, complement proteins, protease inhibitors, hemoglobin proteins etc., they are known to involvein immune system, and complement and coagulation pathway cascade and cholesterol metabolism pathway. Exclusively expressed 22 proteins were found to be disease stage specific and strong PPI network specifically those that have significant role in platelets activation and degranulation, such as Filamin A (FLNA). Furthermore, to validate the mass spectrometric findings, four highly significant proteins (APOA4, SAP, FLNA, and SERPING) were quantified by ELISA in all the study subjects. AUROC analysis showed a significant association of APOA4 (0.830), FLNA (0.958), SAP (0.754) and SERPING (0.600) with the disease. Apolipoprotein A4 (APOA4) has a significant role in cholesterol transport, and in modulation of glucose and lipid metabolism in the CNS. Similarly, FLNA has a crucial role in the nervous system, especially in the functioning of synaptic network. Therefore, both APOA4, and FLNA proteins represent good potential for candidate biomarkers for the diagnosis and prognosis of the intellectual disability. Overall, serum proteome of ID patients provides valuable information of proteins/pathways that are altered during ID progression.

Genetic polymorphisms of apolipoprotein B gene (APOB) may result into serum proteomic perturbance in Coronary Artery Disease (CAD). The current case–control cohort of Pakistani subjects was designed to analyze the genetic influence of APOB rs1042031, (G/T) genotype on serum proteome. Subjects were categorized into two groups: CAD patients (n = 480) and healthy individuals (n = 220). For genotyping, tetra ARMS-PCR was carried out and validated through sequencing, whereas LC/MS-based proteomic analysis of serum samples was performed through label-free quantification. In initial step of genotyping, the frequencies of each genotype GG, GT, and TT were 70%, 27%, and 30% in CAD patients, while in control group, the subjects were 52%, 43%, and 5%, respectively, in CAD patients. The genotypic frequencies in patients vs. control groups found significantly different (p = 0.004), and a strong association of dominant alleles GG with the CAD was observed in both dominant (OR: 2.4 (1.71–3.34), p = 0.001) and allelic genetic models (OR: 2.0 (1.45–2.86), p = 0.001). In second step of label-free quantitation, a total of 40 significant proteins were found with altered expression in CAD patients. The enriched Gene Ontology (GO) terms of molecular functions and pathways of these protein showed upregulated pathways as follows: chylomicron remodeling and assembly, complement cascade activation, plasma lipoprotein assembly, apolipoprotein-A receptor binding, and metabolism of fat-soluble vitamins in G allele carrier of rs1042031 (G > T) vs. mutant T-allele carriers. This study provides better understanding of CAD pathobiology by proteogenomics of APOB. It evidences the influence of APOB rs1042031-dominant (GG) genotype with CAD patients.

Globally, diabetes mellitus is considered as emerging health emergency of twenty-first century. According to the International Diabetic Federation (IDF), 537 million people are currently living with diabetes and this number is projected to increase up to 783 million by 2045 (IDF Report 10th edition 2021). Individuals with type 2 diabetes remain asymptomatic for years or have already lost 50% to 70% of their pancreatic beta cells at the time of diagnosis. The aim of the present study was to discover more sensitive potential protein biomarker candidates capable of predicting progression to hyperglycemia before its onset (at pre-diabetes stage). To accomplish this, nano-liquid chromatography mass spectrometry (nano-LCMS) technique was applied to explore proteome of serum sample from people with normal glucose level (HbA1c ≤ 5.7%), pre-diabetes (HbA1c > 5.7% to < 6.5%), and type 2 diabetes (HbA1c ≥ 6.5%). Differential protein expression of 57 proteins were found between the study groups. Among these 12 proteins were found to be significantly differentially expressed. Proteins SERPINC1, APOA4, AHSG, ITIH4, A2M, VTN, HBB, JCHAIN, and IGHM were found common and with significantly higher expression in both pre-diabetes and diabetes groups as compared to the control. While proteins APOA2, CFB, and APCS were found exclusively significantly high in pre-diabetes group as compared to the diabetes group. The GO analysis resulted that these proteins are known to be performing the functions of modulator proteins, transfer/ carrier proteins, and most importantly are involved in inflammatory processes along with regulation of hydrolases and endopeptidases. The identified proteins hold potential for being early screening/diagnostic biomarker candidates of type 2 diabetes after further validation.

ABSTRACT Apolipoprotein B (APOB) is the major part of low density lipoprotein (LDL), with two major isoforms: APOB100 and APOB48 found in the human body. Both isoforms are involved in the formation and transport of chylomicron and LDL-cholesterol. Point mutations in APOB may lead to change in protein stereochemistry, which may result in premature coronary artery disease, familial hypobetalipoproteinemia, hypocholesterolemia, mono-genic dyslipisimias and other atherogenic events in CVD. Here we evaluated the impact of all missense and non-coding single nucleotide polymorphisms (SNPs) of APOB retrieved from dbSNP using 17 different computational tools and further evaluated the structural impact of these convergent deleterious SNPs on APOB through HOPE. We found 9 missenses, 15 intronic or regulatory region SNPs and 2 were found in miRNA target sites of APOB. Out of these variant, the rs13306194 (Arg532Trp) was found in the conserved region of protein domain, which can potentially disrupt overall chemical structure and function of the APOB. Six missense SNPs in the coding, and 17 SNPs in non-coding regions are proposed as novel most deleterious variants of APOB. We also try to predict the structural model of APOB through protein docking. The results indicate the applicability of in silico approach to propose the most deleterious SNPs of APOB that should be prioritize for future genetic association studies in cohort of cardiovascular patients. While their structural impact on APOB may suggest these predicted nsSNPs possibly be a better drug target and contribute to the treatment and better understanding of human cardiovascular disease.

ABSTRACT The CXCR3 ligand family, including CXCL9, CXCL10 and CXCL11, play important role in T-cell, natural killer (NK) cell, NKT cell and dendritic cell trafficking. These chemokines were suggested for their use as alternative markers for TB diagnosis, and their possible involvement in disease pathophysiology. In the present study, we investigated the genetic association and frequency of CXCL9 and CXCL10 genes polymorphisms in TB patients of Pakistani population. A total of 260 study participants, including 122 with tuberculosis disease (TB) and 138 healthy control (HC) were included. Out of these 122 TB disease subjects (67%) were diagnosed with PTB (n = 82) and 33% with EPTB (n = 40). Tri and Tetra-ARMS-PCR techniques were used to genotype rs2276886 of CXCL9 and rs5606198 of CXCL10 for all the samples. The results showed significant association (OR, 2.1 (0.99 to 4.30), p = 0.04) of A allele of rs2276886 with EPTB group as compared to the control. In genetic model analysis, we found strong association (OR, 0.4 (0.181 to 0.94), p = 0.03) of GG genotype with male with EPTB as compared to female in gender stratified dominant model. Furthermore, in context of different ethnic groups of Pakistani subpopulation, we found Punjabi speaking's to be significantly at higher risk (OR, 2.16 (95%CI, 1.0910 to 4.2901), p = 0.02) of developing TB as compared to other ethnic categories. Strong association of CXCL9 polymorphism (rs2276886 G>A) with TB disease in Pakistani population was concluded especially, Punjabi speaking participants were found to have higher risk of developing TB.

The cross-sectional study was conducted on 200 newly diagnosed diabetic patients to assess the prevalence of diabetic retinopathy and to evaluate its relationship with potential risk factors of diabetes on their first visit to a local hospital. Among the newly diagnosed diabetic subjects 33% (n=66) were with retinopathy. Out of which 30% (n=18) were male and 70% (n=48) were female. High density lipoprotein (HDL), both systolic and diastolic blood pressure, cholesterol level and serum insulin were significantly higher (p < 0.05) in proliferative group of diabetic retinopathy, while triglyceride level, HbA1c (%), and low density lipoprotein (LDL) were non-significantly higher (p[ > 0.05) in diabetic retinopathy groups. To conclude high prevalence of diabetic retinopathy was observed among newly diagnosed diabetic patients.

Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons’ functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer’s disease (AD) and Parkinson’s disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington’s disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-β (Aβ), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases’ advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins—a beneficial approach for drug designing.

Background Huntington’s disease is a rare neurodegenerative illness of the central nervous system that is inherited in an autosomal dominant pattern. Mutant huntingtin protein is produced as a result of enlargement of CAG repeat in the N-terminal of the polyglutamine tract. Aim of the study Herein, we aim to investigate the mutations and their effects on the HTT gene and its genetic variants. Additionally, the protein–protein interaction of HTT with other proteins and receptor-ligand interaction with the three-dimensional structure of huntingtin protein were identified. Methods A comprehensive analysis of the HTT interactome and protein–ligand interaction has been carried out to provide a global picture of structure–function analysis of huntingtin protein. Mutations were analyzed and mutation verification tools were used to check the effect of mutation on protein function. Results The results showed, mutations in a single gene are not only responsible for causing a particular disease but may also cause other hereditary disorders as well. Moreover, the modification at the nucleotide level also cause the change in the specific amino acid which may disrupt the function of HTT and its interacting proteins contributing in disease pathogenesis. Furthermore, the interaction between MECP2 and BDNF lowers the rate of transcriptional activity. Molecular docking further confirmed the strong interaction between MECP2 and BDNF with highest affinity. Amino acid residues of the HTT protein, involved in the interaction with tetrabenazine were N912, Y890, G2385, and V2320. These findings proved, tetrabenazine as one of the potential therapeutic agent for treatment of Huntington’s disease. Conclusion These results give further insights into the genetics of Huntington’s disease for a better understanding of disease models which will be beneficial for the future therapeutic studies.