Mithun Kumar Mitra

Tuberculosis (TB) remains the leading infectious cause of death worldwide, with India accounting for nearly one-fourth of global TB cases. Ni-kshay, the country’s digital case-based TB notification platform is rich in data pertaining to the continuum of care of TB patients. This study aims to develop a standardized analytical approach to programmatic data to identify predictors of unfavourable treatment outcomes and mortality among adult drug-sensitive TB patients at the state level for Maharashtra during 2021 and 2022. Two separate analyses were undertaken comparing treatment success with: (1) unfavourable outcomes (death, treatment failure, loss to follow-up, regimen change, or not evaluated); and (2) mortality. Age group, gender and pre-treatment weight were entered together in a single logistic regression model, and each remaining risk factor was entered into a separate model alongside those three covariates, to compute adjusted odds ratios (aOR). Records with no entry for diabetes, HIV, tobacco or alcohol were retained as a “not recorded” or “unknown” category. Collinearity across variables was assessed using Cramer’s V and variance inflation factors. The final cohort included 323,124 cases for unfavourable outcome analysis and 315,579 cases for mortality analysis. Increasing age, male gender, lower body weight, known HIV and diabetes comorbidities, tobacco and alcohol consumption, and “unknown” status for behavioural risks and comorbidity status were significantly associated with increased odds of both unfavourable outcomes and mortality. This study highlights the utility of programmatic data in identifying high-risk TB patients and offers a reproducible analytic framework.

The back-and-forth (bidirectional) motion of Endosomes and Phagosomes along microtubules is fundamentally important for nutrient absorption, receptor recycling, pathogen degradation and many other downstream cellular pathways. Bidirectional motion requires opposing force from the kinesin and dynein motors, causing intermittent Tug-of-Wars (ToWs) followed by “runs” where winning motors drive their cargo until the next ToW. Dynein displays a counter-intuitive behaviour of catch bonding tightly to the microtubule against opposing force, but why such a response is needed remains unknown. Here we force dynein to catch-bond using an optical trap when dynein-teams are transporting phagosomes in-vitro and inside cells. An unexpected form of collective inactivity emerges, where larger dynein-teams remain inactive for longer after they have been catch-bonded. Kinesin exhibits a high probability of reversing the cargo during such periods of inactivity of dyneins. Therefore, dynein’s inactivity allows cells to auto-regulate the effect of opposing molecular forces for the purpose of achieving bidirectional motion of endosomes/phagosomes. We propose that an inactivating conformational change is induced when dynein detaches against strain from its catch-bonded state. Collective inactivity follows because each inactive dynein exerts an entropic force on the other to delay the entire team from being activated. We capture the dynamics of this process through a statistical model of Entropically Constrained Reactivation, obtaining predictions in excellent agreement with experimental data. The emergent behaviour observed here possibly prevents a “runaway dynein” situation inside cells, its simple entropic origin suggesting broader relevance to catch-bonded receptor ligand interactions operating under conditions of cellular crowding.

Yolk-cytoplasm segregation is among the earliest spatial organization events in the developing embryo of many oviparous animals. The segregation process is intimately linked to early embryonic cleavage and pattern formation, and exhibits a wide range of spatial and temporal diversity. However, the underlying cytoskeletal mechanism remains largely unknown, except for a small number of species. Using quantitative live imaging, we investigated yolk segregation in the Drosophila embryo during the syncytial nuclear cycles 11–14. We find that the yolk vesicles move progressively inward in spatial and temporal coordination with the inward expanding microtubule networks that are nucleated from centrosomes positioned at the cortex, whereas cortical actin meshwork remains spatially restricted. Using the gnu RNAi embryo to decouple nuclear migration and division from cytoskeletal dynamics, we establish causality with targeted pharmacological disruption and find that microtubule dynamics is required for yolk segregation, while depolymerization of actin has no discernible effect. In support of a mechanism of growth-propelled passive displacement, microtubule plus end comets come in apparent contact with yolk vesicles, and injected, inert microbeads are displaced towards the embryo center presumably by the same pushing force. These findings identify microtubule polymerization as a predominant driver of yolk-cytoplasm segregation in Drosophila and suggest that diverse cytoskeletal mechanisms evolved to accomplish this crucial reorganization process.

The mechanisms underlying the scaling of the Bicoid morphogen gradient in Drosophila with the embryo size is not clearly understood. We propose a model with a spatially varying diffusion coefficient along the anterior–posterior axis, that is proportional to the nearly periodic spatial distribution of the nucleo-cytoplasmic domains and alternating between regions of fast and slow diffusion. We postulate that for specific interpretation of the heterogeneous environment, where the space available for free diffusion within an energid is assumed to be proportional to the embryo size, a change in the embryo size can lead to a size-dependent scaling of the gradient lengthscale. We further study a two component model with slow and fast diffusing Bicoid, and identify this model to be equivalent to the heterogeneous diffusion model further postulating that the fast-state occupancy which is a measure of the fraction of time spent in the fast diffusing state, should also scale with the embryo size via the energid size. Finally, we incorporate nuclear shuttling into our model to understand the effect of shuttling on the gradient lengthscale and scaling with embryo size. We argue that for the particular case where the degradation within the nucleus is low, nuclear shuttling does not perturb the Bicoid gradient. Our study suggests that spatial heterogeneity is able to generate scaling, independent of nuclear trapping. This mechanism may act in conjunction with other biological processes, to yield the experimentally observed scaling behavior of the Bicoid gradient.

The early onset of labour increases mortality and developmental risks for a human newborn. Key genes in human labour have been investigated using multiple modalities, but their regulation by non-coding RNA (e.g. lncRNA and miRNA) remains incomplete. This study explores the three-way relationship between labour-associated transcription factors (TFs), miRNA and lncRNA suggested by the competing endogenous RNA (ceRNA) hypothesis, to understand the underlying regulatory framework. Experimentally validated miRNA-lncRNA interactions are modelled using five distinct machine learning (ML) architectures to predict 20469 labour-linked miRNA-lncRNA interactions. Known mRNA-ncRNA interactions from databases were included to construct a tripartite network, and a subset of 9989 labour-linked network motifs containing TFs were isolated and analysed. Gene enrichment of nodes in TF-lncRNA-miRNA network, as well as validation from public myometrial datasets indicate high significance in contractile pathways including immune signalling. Experimentally unconfirmed tripartite network motifs have been found, and we elaborate on their potential regulation in labour using 8 TF-lncRNA-miRNA network motifs. A unified ncRNA-TF regulatory atlas in labour has been synthesized, and a complete summary of the tripartite network motifs can be accessed and visualised using the user-friendly, public database.

Protein molecules must efficiently locate specific DNA sequences within the densely packed chromatin of the cell nucleus. We investigate how the spatial organisation of chromatin, specifically its organisation into Topologically Associating Domains (TADs), fundamentally affects this search process. Using exact analytical theory and simulations of different models of chromatin, we show that target search within compact, highly connected chromatin domains can leverage intersegmental jumps to significantly decrease search times. Further, we establish that there exists an optimal degree of polymer compaction that minimizes the search time for proteins to find their targets. For highly folded domains, our results suggest that rather than bulk diffusion, intersegmental transfers – jumping between chromatin segments that are close together in space – drive the optimal search process. Remarkably, when we analyse 8,355 TAD structures across the human genome, we find that their natural connectivity matches with the theoretical optimum predicted by our model. The structural organisation within TADs significantly reduces protein search times far beyond what is achievable through classical facilitated diffusion. In essence, our work suggests that packaging of chromatin inside the nucleus has implications beyond spatial organisation, and is also intricately linked to the dynamics of proteins inside the nuclear environment.

Lymphatic filariasis (LF) is a mosquito-borne disease caused by filarial worms. The WHO has targeted LF elimination by 2030. Mathematical models can help evaluate and guide elimination strategies. We developed a new compartmental model—COmpartmental Modelling of Elimination strategies and Transmission of Lymphatic Filariasis (COMET-LF)—to assess the impact of mass drug administration (MDA) on LF elimination. Our model incorporates efficacy data from clinical trials and generates estimates of disease (lymphoedema and hydrocele) prevalence. The model is calibrated to publicly available microfilaria (Mf) and disease prevalence data (2008-2013), and validated using programmatic data (2018-2019) from ten endemic districts of Bihar, India. COMET-LF simulation projected that at 85% coverage with a two- or three-drug regimen, six rounds of MDA should be sufficient to achieve elimination for baseline Mf prevalence up to 10%, consistent with the existing models - EPIFIL and LYMFASIM. The impact of MDA on disease burden, generated using COMET-LF, suggest that the disease prevalence reduces over much longer timescales - 20 years for a reduction of 8%-11.5% following 5 rounds of MDA. To investigate the role of migration, we implemented COMET-LF within a meta-population model. We found that high Mf prevalence in the spatial neighbourhood can require up to 3 additional rounds for the two-drug regimen. Migration between non-endemic and adjacent endemic regions (>5% Mf) may lead to a significant risk of resurgence. Our COMET-LF model can capture LF transmission dynamics and estimate the number of MDA rounds required to reach elimination. MDA coverage, baseline Mf prevalences and human migration are the determinants of the required rounds of MDA. Our model can be easily tailored to specific blocks and districts to guide programmatic intervention for LF elimination and disease management.

Lamin proteins, predominantly located near the nuclear lamina, are known to play an important role in maintaining large-scale genome organisation. Disruptions in lamin assembly have been implicated in various diseases exhibiting altered chromatin organisation, nuclear stiffness and chromosome dynamics. Theoretical and computational modelling is essential for understanding these diverse observations through the lens of physical forces and interactions. This review focuses on modelling efforts aimed at elucidating the multifaceted role of lamins in genome organisation and how disruptions in these interactions contribute to the pathologies observed in different cell types, disease conditions and lamin mutants. We categorise the major modelling approaches used to study lamin-mediated chromatin organisation, summarise key findings within each category and highlight future directions in developing a comprehensive understanding of the role of lamins in the spatial organisation of the genome.

Lamins are intermediate filaments constituting the nuclear lamina which maintains the structural integrity of the nucleus and play a key role in the spatiotemporal genome organization. Mutations in lamin A/C have been associated with a plethora of diseases including dilated cardiomyopathy. In this study, we focused on lamin A mutants E161K and K97E which are widely reported in patients afflicted with dilated cardiomyopathy. We established that these mutations cause large scale disruption of the peripheral lamina and consequent heterochromatin (HC) organization, along with the formation of lamin A aggregates inside the nucleoplasm. Using coarse-grained polymer simulations, we explored the interplay between lamin, nuclear membrane, and chromatin showing that disruptions in these interactions can reproduce the experimental phenotypes. Simulations also predict altered positioning depending on HC content and altered lamin dynamics in E161K and K97E mutants. The predictions from the simulations were verified using 3D FISH to delineate the reorganization of chromosome territories in these mutants. Based on the disengagement of lamin A foci from HC, we subsequently investigated the dynamic properties of the mutant aggregates using advanced imaging techniques. Through this, we report that mutant lamin aggregates show internal rearrangement and external exchange at the mesoscopic-level reminiscent of liquid–liquid phase separation.

The application of machine learning (ML) techniques in predictive modelling has greatly advanced our comprehension of biological systems. There is a notable shift in the trend towards integration methods that specifically target the simultaneous analysis of multiple modes or types of data, showcasing superior results compared to individual analyses. Despite the availability of diverse ML architectures for researchers interested in embracing a multimodal approach, the current literature lacks a comprehensive taxonomy that includes the pros and cons of these methods to guide the entire process. Closing this gap is imperative, necessitating the creation of a robust framework. This framework should not only categorize the diverse ML architectures suitable for multimodal analysis but also offer insights into their respective advantages and limitations. Additionally, such a framework can serve as a valuable guide for selecting an appropriate workflow for multimodal analysis. This comprehensive taxonomy would provide a clear guidance and support informed decision-making within the progressively intricate landscape of biomedical and clinical data analysis. This is an essential step towards advancing personalized medicine. The aims of the work are to comprehensively study and describe the harmonization processes that are performed and reported in the literature and present a working guide that would enable planning and selecting an appropriate integrative model. We present harmonization as a dual process of representation and integration, each with multiple methods and categories. The taxonomy of the various representation and integration methods are classified into six broad categories and detailed with the advantages, disadvantages and examples. A guide flowchart describing the step-by-step processes that are needed to adopt a multimodal approach is also presented along with examples and references. This review provides a thorough taxonomy of methods for harmonizing multimodal data and introduces a foundational 10-step guide for newcomers to implement a multimodal workflow.