Meher K. Prakash’s research while affiliated with Jawaharlal Nehru Centre for Advanced Scientific Research and other places

Colonoscopy-based screening provides protection against colorectal cancer (CRC), but the optimal starting age and time intervals of screening colonoscopies are unknown. We aimed to determine an optimal screening schedule for the US population and its dependencies on the objective of screening (life years gained or incidence, mortality, or cost reduction) and the setting in which screening is performed. We used our established open-source microsimulation model CMOST to calculate optimized colonoscopy schedules with one, two, three or four screening colonoscopies between 20 and 90 years of age. A single screening colonoscopy was most effective in reducing life years lost from CRC when performed at 55 years of age. Two, three and four screening colonoscopy schedules saved a maximum number of life years when performed between 49–64 years; 44–69 years; and 40–72 years; respectively. However, for maximum incidence and mortality reduction, screening colonoscopies needed to be scheduled 4–8 years later in life. The optimum was also influenced by adenoma detection efficiency with lower values for these parameters favoring a later starting age of screening. Low adherence to screening consistently favored a later start and an earlier end of screening. In a personalized approach, optimal screening would start earlier for high-risk patients and later for low-risk individuals. In conclusion, our microsimulation-based approach supports colonoscopy screening schedule between 45 and 75 years of age but the precise timing depends on the objective of screening, as well as assumptions regarding individual CRC risk, efficiency of adenoma detection during colonoscopy and adherence to screening.

Gut bacteria play a crucial role in host's metabolism. Both antibiotic and non-antibiotic drugs affect the gut bacteria ecosystem, which negatively affects the host's health. Also, gut bacteria metabolize drugs, making them ineffective to the target. The structure-activity relationship studies of drugs have the scope to make them more effective, efficient, and specific to the target. Previous machine learning studies use the available data to predict the activity of drugs and gut bacteria on each other, but these models lack interpretability. Herein, we study the drug-gut bacteria interaction using interpretable machine learning models. In this study, we identify the most important physicochemical features of the drug, which decide the drug-gut bacteria interactions with each other. One of the key findings of this work is that the higher-positive charged drug molecules can inhibit the growth of gut bacteria more. In contrast, the higher-negative charged drug molecules have higher possibility to get metabolized by gut bacteria.

The relevance of gut bacterial balance to human health can not be overemphasized. The gut bacterial balance delicately relies on several factors inherent to the person as well as to the environment. As the volume of evidences for the gut bacterial influence on health and the clinical data on the variance of the bacterial population across cohorts continue to grow exponentially, it is important to develop a theoretical model for gut bacteria. In this work, we suggest a new computational method for estimating the interaction parameters from the cross-sectional data of bacterial abundances in a cohort, without requiring a longitudinal followup. We introduce a nutrient type based bacterial growth model and use the Monte Carlo approach to estimate the matrix of interaction parameters for the 14 major bacterial species. These parameters were used in a comprehensive first-level computational model we developed for the large intestine to understand the patterns of re-establishing balance with different nutrient types.

A bacterial chemotaxis mechanism is activated when nutrients bind to surface receptors. The sequence of intra- and interprotein events in this signal cascade from the receptors to the eventual molecular motors has been clearly identified. However, the atomistic details remain elusive, as in general may be expected of intraprotein signal transduction pathways, especially when fibrillar proteins are involved. We performed atomistic calculations of the methyl accepting chemoprotein (MCP)-CheA-CheW multidomain complex from Escherichia coli, simulating the methylated and unmethylated conditions in the chemoreceptors and the ATP-bound and apo conditions of the CheA. Our results indicate that these atomistic simulations, especially with one of the two force fields we tried, capture several relevant features of the downstream effects, such as the methylation favoring an intermediate structure that is more toward a dipped state and increases the chance of ATP hydrolysis. The results thus suggest the sensitivity of the model to reflect the nutrient signal response, a nontrivial validation considering the complexity of the system, encouraging even more detailed studies on the thermodynamic quantification of the effects and the identification of the signaling networks.

Several proteins are secreted outside the cell, and in many cases, they may be identified by a characteristic signal peptide. However, more and more studies point to the evidence for an “unconventional” secretion, where proteins without a hitherto unknown signal are secreted, possibly in conditions of starvation. In this work, we analyse a set of 202 RNA binding mammalian proteins, whose unconventional secretion has recently been established. Analysis of these proteins secreted by LC3 mediation, the largest unconventionally secreted dataset to our knowledge, identifies the role of KKX motif as well as triacidic amino acid motif in unconventional secretion, the latter being an extension of the recent implicated diacidic amino acid motif. Further data analysis evolves a hypothesis on the sequence or structural proximity of the triacidic or KKX motifs to the LC3 interacting region, and a phosphorylatable amino acid such as serine as a statistically significant feature among these unconventionally secreted proteins. This hypothesis, although needs to be validated in experiments that challenge the specific details of each of these aspects, appears to be one of the early steps in defining what may be a plausible signal for unconventional protein secretion.

Universal vaccination on an urgent basis is a way of controlling COVID-19 infections and deaths. Vaccine shortage and practical deployment rates on the field necessitate prioritization. The global strategy has been to prioritize those with high personal risk due to their age or comorbidities, and those who constitute the essential workforce of the society. Rather than a systematic age-based rolldown, assigning the next priority requires a local strategy based on vaccine availability, effectiveness of the specific vaccines, population size as well as its age demographics and the scenario of how the pandemic is likely to develop. The adult (2060 yrs) - senior (over 60 yrs) duo from a multigenera-tional home presents a high-risk demographic. The estimated ‘effective age' of an adult living with a grandparent who is not vaccinated may be up to 40 years higher. The proposed model suggests that strategically vaccinating the adults from multigeneration-al homes in India may be effective in saving the lives of around 70,000 to 200,000 seniors, under the different epidemiological scenarios possible with or without strict lockdowns.

Immediate and universal vaccination is a way of controlling the COVID-19 infections and deaths. Shortages of vaccine supplies and practical deployment rates on the field necessitate prioritization. The global strategy has been to prioritize those with a high personal risk due to their age or comorbidities and those who constitute the essential workforce of the society. Rather than a systematic age-based roll-down, assigning the next priority requires a local strategy based on the vaccine availability, the effectiveness of this specific vaccine, the population size as well as its age-demographics, the scenario of how the pandemic is likely to develop. The Adult (ages 20-60) - Senior (ages over 60) duo from a multigenerational home presents a high-risk demographic, with an estimated 'effective age' of an adult to be 40 years more if they live with an unvaccinated grandparent. Our model suggests that strategically vaccinating the Adults from multigenerational homes in India may be effective in saving the lives of around 70,000 to 200,000 of Seniors, under the different epidemiological scenarios possible with or without strict lockdowns.

Mutational effects predictions continue to improve in accuracy as advanced artificial intelligence (AI) algorithms are trained on exhaustive experimental data. The next natural questions to ask are if it is possible to gain insights into which attribute of the mutation contributes how much to the mutational effects and if one can develop universal rules for mapping the descriptors to mutational effects. In this work, we mainly address the former aspect using a framework of interpretable AI. Relations between the physicochemical descriptors and their contributions to the mutational effects are extracted by analyzing the data on 29,832 variants from eight systematic deep mutational scan studies. An opposite trend in the dependence of fitness and solubility on the distance of the amino acid from the catalytic sites could be extracted and quantified. The dependence of the mutational effect contributions on the position-specific scoring matrix (PSSM) score for the amino acid after mutation or the BLOSUM score of the substitution showed universal trends. Our attempts in the present work to explain the quantitative differences in the dependence on conservation and SASA across proteins were not successful. The work nevertheless brings transparency into the predictions and development of rules, and will hopefully lead to empirically uncovering the universalities among these rules.

How does one interpret the observed increase or decrease in COVID19 case rates? Did the compliance to the non pharmaceutical interventions, seasonal changes in the temperature influence the transmission rates or are they purely an artefact of the number of tests? To answer these questions, we estimate the effect sizes from these different factors on the reproduction ratios (Rt) from the different states of the USA during March 9 to August 9. Ideally Rt should be less than 1 to keep the pandemic under control and our model predicts many of these factors contributed significantly to the Rt: Post-lockdown opening of the restaurants and nightclubs contributed 0.04 (CI 0.04-0.04) and 0.11 (CI. 0.11-0.11) to Rt. The mask mandates helped reduce Rt by 0.28 (CI 0.28-0.29)), whereas the testing rates which may have influenced the number of infections observed, did not influence Rt beyond 10,000 daily tests 0.07 (CI -0.57-0.42). In our attempt to understand the role of temperature, the contribution to the Rt was found to increase on both sides of 55 F, which we infer as a reflection of the climatisation needs. A further analysis using the cooling and heating needs showed contributions of 0.24 (CI 0.18-0.31) and 0.31 (CI 0.28-0.33) respectively. The work thus illustrates a data-driven approach for estimating the effect-sizes on the graded policies, and the possibility of prioritising the interventions, if necessary by weighing the economic costs and ease of acceptance with them.

A quantitative COVID-19 model that incorporates hidden asymptomatic patients is developed, and an analytic solution in parametric form is given. The model incorporates the impact of lock-down and resulting spatial migration of population due to announcement of lock-down. A method is presented for estimating the model parameters from real-world data, and it is shown that the various phases in the observed epidemiological data are captured well. It is shown that increase of infections slows down and herd immunity is achieved when active symptomatic patients are 10-25% of the population for the four countries we studied. Finally, a method for estimating the number of asymptomatic patients, who have been the key hidden link in the spread of the infections, is presented.