Androgen Receptor Activation Induces Senescence in Thyroid Cancer Cells.

Thyroid cancer (TC) is the most common endocrine malignancy, with an approximately three-fold higher incidence in women. TCGA data indicate that androgen receptor (AR) RNA is significantly downregulated in PTC. In this study, AR-expressing 8505C (anaplastic TC) (84E7) and K1 (papillary TC) cells experienced an 80% decrease in proliferation over 6 days of exposure to physiological levels of 5α-dihydrotestosterone (DHT). In 84E7, continuous AR activation resulted in G1 growth arrest, accompanied by a flattened, vacuolized cell morphology, with enlargement of the cell and the nuclear area, which is indicative of senescence; this was substantiated by an increase in senescence-associated ß-galactosidase activity, total RNA and protein content, and reactive oxygen species. Additionally, the expression of tumor suppressor proteins p16, p21, and p27 was significantly increased. A non-inflammatory senescence-associated secretory profile was induced, significantly decreasing inflammatory cytokines and chemokines such as IL-6, IL-8, TNF, RANTES, and MCP-1; this is consistent with the lower incidence of thyroid inflammation and cancer in men. Migration increased six-fold, which is consistent with the clinical observation of increased lymph node metastasis in men. Proteolytic invasion potential was not significantly altered, which is consistent with unchanged MMP/TIMP expression. Our studies provide evidence that the induction of senescence is a novel function of AR activation in thyroid cancer cells, and may underlie the protective role of AR activation in the decreased incidence of TC in men.

Efficacy and safety of biosimilar versus originator infliximab in patients with inflammatory bowel disease: A real-world cohort analysis.

BACKGROUND: Anti-tumor necrosis factor (anti-TNF) monoclonal antibody, infliximab, is the primary therapeutic modality for patients with Crohn's disease (CD) and ulcerative colitis (UC), refractory to conventional therapy. Biosimilars of infliximab have been shown to have equivalent efficacy to originator infliximab. We compared the safety and efficacy of infliximab biosimilar with the originator in Indian patients with inflammatory bowel disease (IBD). METHODS: Patients with IBD treated with either originator or biosimilar infliximab from January 2005 to October 2020 were included in this retrospective analysis. The safety and efficacy of originator or biosimilar infliximab in inducing and maintaining clinical remission at weeks 14 and 52 for CD and UC were evaluated. Disease activity was estimated at baseline, after induction therapy, after 1 year of treatment, and during 12 months of follow-up. RESULTS: In all, 137 patients (82 CD; 55 UC) were included, of whom 102 were on originator, and 35 patients received biosimilar. In biosimilar group, clinical response and remission rates at weeks 14 and 52 were 84.2%, 58% and 68.4%, 52.6% in CD and 81.2%, 56.2% and 68.7%, 62.5% in UC patients, respectively. Among patients who were on originator, clinical response and remission rates at weeks 14 and 52 were 79.4%, 46% and 57.1%, 43% in CD and 72%, 64.1% and 66.7%, 56.4% in UC patients, respectively. Thirty-three (24.1%) patients experienced adverse events; eighteen developed tuberculosis (TB), of whom 17 received originator and one patient received biosimilar. CONCLUSIONS: Infliximab biosimilar is comparable to originator infliximab in terms of safety profile and its efficacy in inducing and maintaining remission in patients with IBD.

COVID-19 infection, and reinfection, and vaccine effectiveness against symptomatic infection among health care workers in the setting of omicron variant transmission in New Delhi, India.

Background: Surge of SARS CoV-2 infections ascribed to omicron variant began in December 2021 in New Delhi. We determined the infection and reinfection density in a cohort of health care workers (HCWs) along with vaccine effectiveness (VE) against symptomatic infection within omicron transmission period (considered from December 01, 2021 to February 25, 2022. Methods: This is an observational study from the All India Institute of Medical Sciences, New Delhi. Data were collected telephonically. Person-time at risk was counted from November 30, 2021 till date of infection/ reinfection, or date of interview. Comparison of clinical features and severity was done with previous pandemic periods. VE was estimated using test-negative case-control design [matched pairs (for age and sex)]. Vaccination status was compared and adjusted odds ratios (OR) were computed by conditional logistic regression. VE was estimated as (1-adjusted OR)X100-. Findings: 11474 HCWs participated in this study. The mean age was 36⋅2 (±10⋅7) years. Complete vaccination with two doses were reported by 9522 (83%) HCWs [8394 (88%) Covaxin and 1072 Covishield (11%)]. The incidence density of all infections and reinfection during the omicron transmission period was 34⋅8 [95% Confidence Interval (CI): 33⋅5-36⋅2] and 45⋅6 [95% CI: 42⋅9-48⋅5] per 10000 person days respectively. The infection was milder as compared to previous periods. VE was 52⋅5% (95% CI: 3⋅9-76⋅5, p = 0⋅036) for those who were tested within 14-60 days of receiving second dose and beyond this period (61-180 days), modest effect was observed. Interpretation: Almost one-fifth of HCWs were infected with SARS CoV-2 during omicron transmission period, with predominant mild spectrum of COVID-19 disease. Waning effects of vaccine protection were noted with increase in time intervals since vaccination. Funding: None.

Stringent screening strategy significantly reduces reactivation rates of tuberculosis in patients with inflammatory bowel disease on anti-TNF therapy in tuberculosis endemic region.

BACKGROUND: Anti-tumor necrosis factor (anti-TNF) therapy use in patients with inflammatory bowel disease (IBD) leads to an increased risk of tuberculosis (TB) reactivation despite latent tuberculosis (LTB) screening, especially in TB endemic regions. AIM: We evaluated the effect of stringent screening strategy and LTB prophylaxis on TB reactivation. METHODS: We performed an ambispective comparison between patients who received anti-TNF therapy after January 2019 (late cohort) and between Jan 2005 and Jan 2019 (early cohort). Late cohort patients were subjected to stringent screening criteria which included all: history of past TB/recent contact with active TB, chest X-ray, CT (computed tomography) chest, IGRA (interferon-gamma release assay), TST (tuberculin skin test), and if any positive were given chemoprophylaxis. A cohort comparison was done to evaluate for risk reduction of TB following the stringent screening strategy. RESULTS: One hundred seventy-one patients (63: ulcerative colitis/108: Crohn's disease, mean age diagnosis: 28.5 ± 13.4 years, 60% males, median follow-up duration after anti-TNF: 33 months [interquartile range: 23-57 months]) were included. Among the 112 in the early cohort, 29 (26%) underwent complete TB screening, 22 (19.6%) had LTB, 10 (9%) received chemoprophylaxis, and 19 (17%) developed TB. In comparison, in the late cohort, 100% of patients underwent complete TB screening, 26 (44%) had LTB, 23 (39%) received chemoprophylaxis, and only 1(1.7%) developed TB (p < 0.01). On survival analysis, patients in early cohort had a higher probability of TB reactivation compared with the late cohort (HR: 14.52 (95% CI: 1.90-110.61 [p = 0.01]) after adjusting for gender, age at anti-TNF initiation, concomitant immunosuppression, anti-TNF doses, and therapy escalation. CONCLUSION: The high risk of TB reactivation with anti-TNF therapy in TB endemic regions can be significantly mitigated with stringent LTB screening and chemoprophylaxis.

SARS-CoV-2 Reinfection Rate and Estimated Effectiveness of the Inactivated Whole Virion Vaccine BBV152 Against Reinfection Among Health Care Workers in New Delhi, India.

Importance: A surge of COVID-19 occurred from March to June 2021, in New Delhi, India, linked to the B.1.617.2 (Delta) variant of SARS-CoV-2. COVID-19 vaccines were rolled out for health care workers (HCWs) starting in January 2021. Objective: To assess the incidence density of reinfection among a cohort of HCWs and estimate the effectiveness of the inactivated whole virion vaccine BBV152 against reinfection. Design, Setting, and Participants: This was a retrospective cohort study among HCWs working at a tertiary care center in New Delhi, India. Exposures: Vaccination with 0, 1, or 2 doses of BBV152. Main Outcomes and Measures: The HCWs were categorized as fully vaccinated (with 2 doses and ≥15 days after the second dose), partially vaccinated (with 1 dose or 2 doses with <15 days after the second dose), or unvaccinated. The incidence density of COVID-19 reinfection per 100 person-years was computed, and events from March 3, 2020, to June 18, 2021, were included for analysis. Unadjusted and adjusted hazard ratios (HRs) were estimated using a Cox proportional hazards model. Estimated vaccine effectiveness (1 - adjusted HR) was reported. Results: Among 15 244 HCWs who participated in the study, 4978 (32.7%) were diagnosed with COVID-19. The mean (SD) age was 36.6 (10.3) years, and 55.0% were male. The reinfection incidence density was 7.26 (95% CI: 6.09-8.66) per 100 person-years (124 HCWs [2.5%], total person follow-up period of 1696 person-years as time at risk). Fully vaccinated HCWs had lower risk of reinfection (HR, 0.14 [95% CI, 0.08-0.23]), symptomatic reinfection (HR, 0.13 [95% CI, 0.07-0.24]), and asymptomatic reinfection (HR, 0.16 [95% CI, 0.05-0.53]) compared with unvaccinated HCWs. Accordingly, among the 3 vaccine categories, reinfection was observed in 60 of 472 (12.7%) of unvaccinated (incidence density, 18.05 per 100 person-years; 95% CI, 14.02-23.25), 39 of 356 (11.0%) of partially vaccinated (incidence density 15.62 per 100 person-years; 95% CI, 11.42-21.38), and 17 of 1089 (1.6%) fully vaccinated (incidence density 2.18 per 100 person-years; 95% CI, 1.35-3.51) HCWs. The estimated effectiveness of BBV152 against reinfection was 86% (95% CI, 77%-92%); symptomatic reinfection, 87% (95% CI, 76%-93%); and asymptomatic reinfection, 84% (95% CI, 47%-95%) among fully vaccinated HCWs. Partial vaccination was not associated with reduced risk of reinfection. Conclusions and Relevance: These findings suggest that BBV152 was associated with protection against both symptomatic and asymptomatic reinfection in HCWs after a complete vaccination schedule, when the predominant circulating variant was B.1.617.2.

Computational prediction and validation of specific EmbR binding site on PknH.

Tuberculosis drug resistance continues to threaten global health but the underline molecular mechanisms are not clear. Ethambutol (EMB), one of the well-known first - line drugs in tuberculosis treatment is, unfortunately, not free from drug resistance problems. Genomic studies have shown that some genetic mutations in Mycobacterium tuberculosis (Mtb) EmbR, and EmbC/A/B genes cause EMB resistance. EmbR-PknH pair controls embC/A/B operon, which encodes EmbC/A/B genes, and EMB interacts with EmbA/B proteins. However, the EmbR binding site on PknH was unknown. We conducted molecular simulation on the EmbR- peptides binding structures and discovered phosphorylated PknH 273-280 (N'-HEALSPDPD-C') makes ß strand with the EmbR FHA domain, as ß-MoRF (MoRF; molecular recognition feature) does at its binding site. Hydrogen bond number analysis also supported the peptides' ß-MoRF forming activity at the EmbR FHA domain. Also, we discovered that previously known phosphorylation residues might have their chronological order according to the phosphorylation status. The discovery validated that Mtb PknH 273-280 (N'-HEALSDPD-C') has reliable EmbR binding affinity. This approach is revolutionary in the computer-aided drug discovery field, because it is the first trial to discover the protein-protein interaction site, and find binding partner in nature from this site.

Extending the "resting state hypothesis of depression" - dynamics and topography of abnormal rest-task modulation.

Major depressive disorder (MDD) is characterized by changes in both rest and task states as manifested in temporal dynamics (EEG) and spatial patterns (fMRI). Are rest and task changes related to each other? Extending the "Resting state hypothesis of depression" (RSHD) (Northoff et al., 2011), we, using multimodal imaging, take a tripartite approach: (i) we conduct a review of EEG studies in MDD combining both rest and task states; (ii) we present our own EEG data in MDD on brain dynamics, i.e., intrinsic neural timescales as measured by the autocorrelation window (ACW); and (iii) we review fMRI studies in MDD to probe whether different regions exhibit different rest-task modulation. Review of EEG data shows reduced rest-task change in MDD in different measures of temporal dynamics like peak frequency (and others). Notably, our own EEG data show decreased rest-task change as measured by ACW in frontal electrodes of MDD. The fMRI data reveal that different regions exhibit different rest-task relationships (normal rest-abnormal task, abnormal rest-normal task, abnormal rest-abnormal task) in MDD. Together, we demonstrate altered spatiotemporal dynamics of rest-task modulation in MDD; this further supports and extends the key role of the spontaneous activity in MDD as proposed by the RSHD.

Androgen Activity Is Associated With PD-L1 Downregulation in Thyroid Cancer.

Thyroid cancer is the most prevalent endocrine malignancy in the United States with greater than 53,000 new cases in 2020. There is a significant gender disparity in disease incidence as well, with women developing thyroid cancer three times more often than men; however, the underlying cause of this disparity is poorly understood. Using RNA-sequencing, we profiled the immune landscape of papillary thyroid cancer (PTC) and identified a significant inverse correlation between androgen receptor (AR) levels and the immune checkpoint molecule PD-L1. The expression of PD-L1 was then measured in an androgen responsive-thyroid cancer cell line. Dihydrotestosterone (DHT) treatment resulted in significant reduction in surface PD-L1 expression in a time and dose-dependent manner. To determine if androgen-mediated PD-L1 downregulation was AR-dependent, we treated cells with flutamide, a selective AR antagonist, and prior to DHT treatment to pharmacologically inhibit AR-induced signaling. This resulted in a > 90% restoration of cell surface PD-L1 expression, suggesting a potential role for AR activity in PD-L1 regulation. Investigation into the AR binding sites showed AR activation impacts NF-kB signaling by increasing IkBα and by possibly preventing NF-kB translocation into the nucleus, reducing PD-L1 promoter activation. This study provides evidence of sex-hormone mediated regulation of immune checkpoint molecules in vitro with potential ramification for immunotherapies.

Androgen receptor activation decreases proliferation in thyroid cancer cells.

The American Cancer Society predicted more than 52 000 new cases of thyroid cancer in 2020, making it the most prevalent endocrine malignancy. Due to the approximately threefold higher incidence of thyroid cancer in women, we hypothesize that androgens and/or androgen receptors play a protective role and that thyroid cancer in men represents an escape from androgen-mediated cell regulation. The analysis of androgen receptor (AR) expression in patient tissue samples identified a 2.7-fold reduction in AR expression (p < 0.005) in papillary thyroid cancer compared with matched, normal tissue. An in vitro cell model was developed by stably transfecting AR into 8505C undifferentiated thyroid cancer cells (resulting in clone 84E7). The addition of DHT to the clone 84E7 resulted in AR translocation into the nucleus and a 70% reduction in proliferation, with a shift in the cell cycle toward G1 arrest. RNASeq analysis revealed significant changes in mRNA levels associated with proliferation, cell cycle, and cell cycle regulation. Furthermore, androgen significantly decreased the levels of the G1-associated cell cycle progression proteins cdc25a CDK6 CDK4 and CDK2 as well as increased the levels of the cell cycle inhibitors, p27 and p21. The data strongly suggest that DHT induces a G1 arrest in androgen-responsive thyroid cancer cells. Together, these data support our hypothesis that AR/androgen may play a protective, antiproliferative role and are consistent with younger men having a lower incidence of thyroid cancer than women.

Revolutionizing enzyme engineering through artificial intelligence and machine learning.

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

Generative Recurrent Networks for De Novo Drug Design.

Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets.

A kinetic platform for in silico modeling of the metabolic dynamics in Escherichia coli.

BACKGROUND: A prerequisite for a successful design and discovery of an antibacterial drug is the identification of essential targets as well as potent inhibitors that adversely affect the survival of bacteria. In order to understand how intracellular perturbations occur due to inhibition of essential metabolic pathways, we have built, through the use of ordinary differential equations, a mathematical model of 8 major Escherichia coli pathways. RESULTS: Individual in vitro enzyme kinetic parameters published in the literature were used to build the network of pathways in such a way that the flux distribution matched that reported from whole cells. Gene regulation at the transcription level as well as feedback regulation of enzyme activity was incorporated as reported in the literature. The unknown kinetic parameters were estimated by trial and error through simulations by observing network stability. Metabolites, whose biosynthetic pathways were not represented in this platform, were provided at a fixed concentration. Unutilized products were maintained at a fixed concentration by removing excess quantities from the platform. This approach enabled us to achieve steady state levels of all the metabolites in the cell. The output of various simulations correlated well with those previously published. CONCLUSION: Such a virtual platform can be exploited for target identification through assessment of their vulnerability, desirable mode of target enzyme inhibition, and metabolite profiling to ascribe mechanism of action following a specific target inhibition. Vulnerability of targets in the biosynthetic pathway of coenzyme A was evaluated using this platform. In addition, we also report the utility of this platform in understanding the impact of a physiologically relevant carbon source, glucose versus acetate, on metabolite profiles of bacterial pathogens.

Structural models in the assessment of protein druggability based on HTS data.

Insights on the potential of target proteins to bind small molecules with high affinity can be derived from the knowledge of their three-dimensional structural details especially of their binding pockets. The present study uses high-throughput screening (HTS) results on various targets, to obtain mathematical predictive models in which a minimal set of structural parameters significantly contributing to the hit rates or the affinity of the protein binding pockets for small molecular entities, is identified. An emphasis is given to focus on target variation aspect of the data by consideration of commonly tested compounds against the HTS targets. We identify 'four-parameter' models with R (2), [Formula: see text], SEE, and LOO q (2) values of 0.70, 0.60, 0.27 and 0.50, respectively, or better. We demonstrate through cross-validation exercises that our regression models apply well on varied data sets. Thus we can use these models to estimate hit rates for HTS campaigns and thereby assign priority to drug targets before they undergo such resource intense experimental screening and follow-up.