Jyoti Bajpai Dikshit

Proteomic Investigation of Falciparum and Vivax Malaria for Identification of Surrogate Protein Markers

This study was conducted to analyze alterations in the human serum proteome as a consequence of infection by malaria parasites Plasmodium falciparum and P. vivax to obtain mechanistic insights about disease pathogenesis, host immune response, and identification of potential protein markers. Serum samples from patients diagnosed with falciparum malaria (FM) (n = 20), vivax malaria (VM) (n = 17) and healthy controls (HC) (n = 20) were investigated using multiple proteomic techniques and results were validated by employing immunoassay-based approaches. Specificity of the identified malaria related serum markers was evaluated by means of analysis of leptospirosis as a febrile control (FC). Compared to HC, 30 and 31 differentially expressed and statistically significant (p<0.05) serum proteins were identified in FM and VM respectively, and almost half (46.2%) of these proteins were commonly modulated due to both of the plasmodial infections. 13 proteins were found to be differentially expressed in FM compared to VM. Functional pathway analysis involving the identified proteins revealed the modulation of different vital physiological pathways, including acute phase response signaling, chemokine and cytokine signaling, complement cascades and blood coagulation in malaria. A panel of identified proteins consists of six candidates; serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I was used to build statistical sample class prediction models. By employing PLS-DA and other classification methods the clinical phenotypic classes (FM, VM, FC and HC) were predicted with over 95% prediction accuracy. Individual performance of three classifier proteins; haptoglobin, apolipoprotein A-I and retinol-binding protein in diagnosis of malaria was analyzed using receiver operating characteristic (ROC) curves. The discrimination of FM, VM, FC and HC groups on the basis of differentially expressed serum proteins demonstrates the potential of this analytical approach for the detection of malaria as well as other human diseases.

Investigation of serum proteome alterations in human glioblastoma multiforme

Glioblastoma multiforme (GBM) or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. The present study was conducted to investigate the alterations in the serum proteome in GBM patients compared to healthy controls. Comparative proteomic analysis was performed employing classical 2DE and 2D‐DIGE combined with MALDI TOF/TOF MS and results were further validated through Western blotting and immunoturbidimetric assay. Comparison of the serum proteome of GBM and healthy subjects revealed 55 differentially expressed and statistically significant (p <0.05) protein spots. Among the identified proteins, haptoglobin, plasminogen precursor, apolipoprotein A‐1 and M, and transthyretin are very significant due to their functional consequences in glioma tumor growth and migration, and could further be studied as glioma biomarkers and grade‐specific protein signatures. Analysis of the lipoprotein pattern indicated elevated serum levels of cholesterol, triacylglycerol, and low‐density lipoproteins in GBM patients. Functional pathway analysis was performed using multiple software including ingenuity pathway analysis (IPA), protein analysis through evolutionary relationships (PANTHER), database for annotation, visualization and integrated discovery (DAVID), and GeneSpring to investigate the biological context of the identified proteins, which revealed the association of candidate proteins in a few essential physiological pathways such as intrinsic prothrombin activation pathway, plasminogen activating cascade, coagulation system, glioma invasiveness signaling, and PI3K signaling in B lymphocytes. A subset of the differentially expressed proteins was applied to build statistical sample class prediction models for discrimination of GBM patients and healthy controls employing partial least squares discriminant analysis (PLS‐DA) and other machine learning methods such as support vector machine (SVM), Decision Tree and Naïve Bayes, and excellent discrimination between GBM and control groups was accomplished.

A systems biology based integrative framework to enhance the predictivity of in vitro methods for drug-induced liver injury

Background: Liver injury is the most common cause of postmarketing withdrawal of drugs. Traditional animal toxicity testing methods have proved to be imperfect tools for predicting toxicity observed in the clinic. Objective: Predictive methods that integrate data and insights from several in vitro methods to provide a deeper understanding of the impact of a drug on the liver are the need of the hour. Method: A systems approach based on mathematical modelling using the kinetics of biochemical pathways involved in liver homeostasis coupled with in vitro measurements to quantify drug-induced perturbations is described here. Conclusions: Integrating in silico and in vitro methods provides a powerful platform that allows reasonably accurate and mechanistic-level prediction of drug-induced liver injury. The method demonstrates that several physiological situations can be accurately modelled as can the effect of perturbations induced by drugs. It can also be used along with high-throughput ‘omic’ data to generate testable hypotheses leading to informed decision-making.

Identification of a novel glucokinase mutation in an Indian woman with GCK-MODY

Glucokinase-maturity-onset diabetes of the young (GCK-MODY; also known as MODY 2) is believed to cause 1–2% of cases diagnosed as gestational diabetes. Pregnant women with GCK-MODY should be diff erentiated from those with gestational diabetes, because diff er ent management is needed. The prevalence of GCK-MODY in Asians is unclear because of a paucity of epidemiological data, although Rudland and colleagues estimated a prevalence of about 1–1·9 per 100 for Indian women diagnosed with gestational diabetes. We identifi ed a 29-year-old Indian woman with mild fasting hyperglycemia during pregnancy. 1 year before gestation, routine biochemical testing had shown fasting plasma glucose (FPG) of 6·1 mmol/L. At this time, a 2-h oral glucose tolerance test had shown an FPG concentration of 6·9 mmol/L and 2-h FPG concentration of 7·8 mmol/L. Further testing before pregnancy showed a fasting C-peptide concentration of 0·9 ng/mL, an HbA1c of 48 mmol/mol (6·5%), a fasting insulin concentration of 6 μIU/mL, a GAD-65 autoantibody titre of less than 5 IU/mL, an islet cell antibody titre of less than 1:4, and a BMI of 19 kg/m2. A diagnosis of diabetes was then made on the basis of the HbA1c measurement before pregnancy. However, negative antibody titres and an absence of clinical or biochemical features consistent with insulin resistance precluded type 1 or type 2 diabetes. Genetic testing before pregnancy identifi ed a variant in the GCK gene: c.1030G>T; p.Asp344Tyr, but it was not known whether this variant was clinically relevant. After conception, FPG was about 6·1 mmol/L. We expected glucose levels to come down after conception, which usually happens in a normal pregnancy. However, when FPG remained high during the fi rst trimester, and BMI being low, we made the diagnosis of GCK-MODY and began monitoring fetal size. Fetal growth was monitored with serial ultrasounds, and the patient was managed with lifestyle modifi cation alone. 2-h postprandial plasma glucose remained in the 6·1–6·7 mmol/L range. Since the fetus was growing appropriately, we assumed that it had inherited the same mutation, because had it not inherited the mutation, it would be at high risk of macrosomia. At 38 weeks gestation, the patient gave birth to a healthy boy (3·3 kg). The baby did not develop macrosomia, possibly in part because the mother’s glycaemic control was overall at target. Direct maternal gene sequencing (saliva) was repeated, confi rming a heterozygous variant of unknown clinical signifi cance in exon 9 of the GCK gene (chr7:44185319C>A, c.1030G>T, p.Asp344Tyr). The variant was in the vicinity of other missense variants that are probably pathogenic (p.Ser340Gly and p.Ile348Asn), and was predicted to be damaging because of its conserved nature and proximity to other previously reported pathogenic variants. This variant is not among the known 620 GCK mutations that have been identifi ed in 1441 families. We did a family segregation analysis in the patient’s immediate family members, as well as her newborn baby (her spouse was not tested because fasting hyperglycemia had not been documented). The patient’s father and brother had the same mutation. Her 54-year-old father (BMI 29·5 kg/m2) had an FPG of 6·6 mmol/L and an HbA1c of 6·5%. Her 20-year-old brother (BMI 28·65 kg/m2) had an FPG of 6·3 mmol/L and an HbA1c of 6·0%. The baby was found not to have inherited the mutation and was not tested for diabetes. To our knowledge, this GCK variant has never been described in any ethnic group. We believe that the variant is pathogenic, since all aff ected family members have FBG and HbA1c measurements consistent with a GCK-MODY phenotype.