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Summary Here, we describe a robust protocol using mouse models to screen potential insulin-stabilizers and insulin moieties. We have generated a mouse model of amyloidoma, found in diabetic patients undergoing insulin therapy. This model can be used to screen potential insulin stabilizers and insulin moieties to prevent amyloidoma formation. This protocol can further be used for the preclinical validation of therapeutically relevant insulin stabilizers and formulations. The protocol highlights all the critical steps for generating amyloidoma in a preclinical model. For complete details on the use and execution of this profile, please refer to Mukherjee et al. (2021)., Graphical abstract, Highlights • We present a detailed protocol for the generation of subcutaneous amyloidoma in mice • This protocol facilitates screening of novel insulin-stabilizing molecules in vivo • This approach can be adapted to study amyloidosis in other amyloid-related diseases, Here, we describe a robust protocol using mouse models to screen potential insulin-stabilizers and insulin moieties. We have generated a mouse model of amyloidoma, found in diabetic patients undergoing insulin therapy. This model can be used to screen potential insulin stabilizers and insulin moieties to prevent amyloidoma formation. This protocol can further be used for the preclinical validation of therapeutically relevant insulin stabilizers and formulations. The protocol highlights all the critical steps for generating amyloidoma in a preclinical model.

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period.

Identification of the genomic diversity and the phylodynamic profiles of prevalent variants is critical to understand the evolution and spread of SARS-CoV-2 variants. We performed whole-genome sequencing of 54 SARS-CoV-2 variants collected from COVID-19 patients in Kolkata, West Bengal during August–October 2020. Phylogeographic and phylodynamic analyses were performed using these 54 and other sequences from India and abroad that are available in the GISAID database. We estimated the clade dynamics of the Indian variants and compared the clade-specific mutations and the co-mutation patterns across states and union territories of India over the time course. Frequent mutations and co-mutations observed within the major clades across time periods do not show much overlap, indicating the emergence of newer mutations in the viral population prevailing in the country. Furthermore, we explored the possible association of specific mutations and co-mutations with the infection outcomes manifested in Indian patients.

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

Background: India has been amongst the most affected nations during the SARS CoV2 pandemic, with sparse data on country wide spread of asymptomatic infections and antibody persistence. This longitudinal cohort study was aimed to evaluate SARS CoV2 seropositivity rate as a marker of infection and evaluate temporal persistence of antibodies with neutralization capability and to infer possible risk factors for infection. Methods: Council of Scientific and Industrial Research, India (CSIR) with its more than 40 laboratories and centers in urban and semi urban settings spread across the country piloted the pan country surveillance. 10427 adult individuals working in CSIR laboratories and their family members based on voluntary participation were assessed for antibody presence and stability was analyzed over 6 months utilizing qualitative Elecysys SARS CoV2 specific antibody kit and GENScript cPass SARS CoV2 Neutralization Antibody Detection Kit. Along with demographic information, possible risk factors were evaluated through self to be filled online forms with data acquired on blood group type, occupation type, addiction and habits including smoking and alcohol, diet preferences, medical history and transport type utilized. Symptom history and information on possible contact and compliance with COVID 19 universal precautions was also obtained. Findings:1058 individuals (10.14%) had antibodies against SARS CoV2. A follow up on 346 seropositive individuals after three months revealed stable to higher antibody levels against SARS CoV2 but declining plasma activity for neutralizing SARS CoV2 receptor binding domain and ACE2 interaction. A repeat sampling of 35 individuals, at six months, revealed declining antibody levels while the neutralizing activity remained stable compared to three months. Majority of seropositive individuals (75%) did not recall even one of nine symptoms since March 2020. Fever was the most common symptom with one fourth reporting loss of taste or smell. Significantly associated risks for seropositivity (Odds Ratio, 95% CI, p value) were observed with usage of public transport (1.79, 1.43 to 2∙24, 2.81561x10-6), occupational responsibilities such as security, housekeeping personnel etc. (2.23, 1.92 to 2.59, 6.43969x10-26), non smokers (1.52, 1.16 to 1.99, 0.02) and non vegetarianism (1.67, 1.41 to 1.99, 3.03821x10-8). An iterative regression analysis was confirmatory and led to only modest changes to estimates. Predilections for seropositivity was noted with specific ABO blood groups; O was associated with a lower risk. Interpretation: In a first of its kind study from India, we report the seropositivity in a country wide cohort and identify variable susceptible associations for contacting infection. Serology and Neutralizing Antibody response provides much sought for general insights on the immune response to the virus among Indians and will be an important resource for designing vaccination strategies. Funding: Council of Scientific and Industrial Research, India (CSIR)

Emergence of distinct viral clades has been observed in SARS-CoV2 variants across the world and India. Identification of the genomic diversity and the phylodynamic profiles of the prevalent strains of the country are critical to understand the evolution and spread of the variants. We performed whole-genome sequencing of 54 SARS-CoV2 strains collected from COVID-19 patients in Kolkata, West Bengal during August to October 2020. Phylogeographic and phylodynamic analyses were performed using these 54 and other sequences from India and abroad available in GISAID database. Spatio-temporal evolutionary dynamics of the pathogen across various regions and states of India over three different time periods in the year 2020 were analyzed. We estimated the clade dynamics of the Indian strains and compared the clade specific mutations and the co-mutation patterns across states and union territories of India over the time course. We observed that GR, GH and G (GISAID) or 20B and 20A (Nextstrain) clades were the prevalent clades in India during middle and later half of the year 2020. However, frequent mutations and co-mutations observed within the major clades across time periods do not show much overlap, indicating emergence of newer mutations in the viral population prevailing in the country. Further, we explored the possible association of specific mutations and co-mutations with the infection outcomes manifested within the Indian patients.

Aims: Obesity associated prolonged hyperinsulinemia followed by β-cell failure is well established as the pathology behind type 2 diabetes mellitus (T2DM). However, studies on nonobese T2DM have reported it to be a distinct clinical entity with predominant insulin secretory defect. We, therefore, hypothesized that compensatory hyperinsulinemia in response to weight gain is impaired in nonobese subjects. Methods: This was a cross-sectional study from a community-based metabolic health screening program. Adiposity parameters including body mass index (BMI), waist circumference (WC), body fat percentage, plasma leptin concentration and metabolic parameters namely fasting insulin, glucose, total cholesterol, and triglycerides were measured in 650 individuals (73% healthy, 62% nonobese with a BMI Results: In contrast to obese T2DM, nonobese T2DM patients did not exhibit significant hyperinsulinemia compared with the nonobese healthy group. Age, sex, and fasting glucose adjusted insulin levels, homeostatic model assessment of insulin resistance (HOMA-IR) and HOMA-beta cell function (HOMA-B) were increased in obese T2DM compared with nonobese T2DM. Although adiposity parameters showed strong correlation with fasting insulin in obese healthy ( r = 0.38, 0.38, and 0.42, respectively; all p values Conclusions: Compensatory hyperinsulinemia in response to weight gain is impaired in the nonobese population making insulin secretory defect rather than IR the major pathology behind nonobese T2DM.

Incidence of hepatotoxicity following acute drug-induced proteasomal inhibition and development of chronic proteasome dysfunction in obesity and insulin resistance underscores the crucial importance of hepatic protein homeostasis albeit with an elusive molecular basis and therapeutic opportunities. Apart from lipotoxicity and endoplasmic reticulum (ER) stress, herein we report that hepatocytes are highly susceptible to proteasome-associated metabolic stress attune to altered redox homeostasis. Bortezomib-induced proteasomal inhibition caused severe hepatocellular injury independent of ER stress via proapoptotic Apoptosis Signal-regulating Kinase 1 (ASK1)- c-Jun N-terminal kinase (JNK1)- p38 signaling concomitant with inadequate peroxisome proliferator-activated receptor γ (PPARγ)- Nuclear factor erythroid 2-related factor 2 (Nrf2) -driven antioxidant response. Although inhibition of ASK1 rescued acute hepatotoxicity, hepatic depletion of PPARγ or its physiological activator pigment epithelium-derived factor (PEDF) further aggravated liver injury even under ASK1 inhibition, emphasizing that endogenous PPARγ driven antioxidant activity serves as a prerequisite for the favorable therapeutic outcome of ASK1 inhibition. Consequently, ASK1 inhibitor selonsertib and PPARγ agonist pioglitazone in pharmacological synergism ameliorated bortezomib-induced hepatotoxicity and significantly prolonged survival duration in mice. Moreover, we showed that proteasome dysfunction is associated with ASK1 activation and insufficient PPARγ/Nrf2-driven antioxidative response in a subset of human nonalcoholic steatohepatitis (NASH) patients and the preclinical NASH model. The latter remains highly responsive to the drug combination marked by revamped proteasomal activity and alleviated hallmarks of NASH such as steatosis, fibrosis, and hepatocellular death. We thus uncovered a pharmacologically amenable interdependent binodal molecular circuit underlying hepatic proteasomal dysfunction and associated oxidative injury., Graphical abstract Image 1, Highlights • Acute proteasomal inhibition causes severe hepatocellular injury independent of ER stress. • Proteasome dysfunction dictates metabolic stress and liver injury in NASH. • ASK1 activation steers proteotoxic hepatocellular death under compromised PPARγ activity. • Selonsertib and pioglitazone combination therapy prolong survival in proteotoxic hepatic failure. • Combination therapy revamped proteasome activity and mitigate liver injury in NASH preclinical trial.