Basant K. Tiwary

The present in silico study provided the adaptive binding of seven physiologically active molecules, including four non-peptide hormones (cortisol, estradiol, triiodothyronine, melatonin) and three vitamins (methyltetrahydrofolate, pyridoxal phosphate, thiamine), alongside argireline as a benchmark, at hotspot-enriched interfaces of full C2AB-domain of Synaptotagmin 1 (Syt1), using molecular docking study and molecular dynamics (MD) simulations. Linear correlations of MD-features and nonlinear KPCA-IG–based ranking of features and ligands yielded deeper mechanistic insights. All ligands exhibited spontaneous binding with strong negative ΔG _binding values. Fifteen hotspots, clustered across the C2A, C2B, and C2AB–interlink regions, were dominated by hydrophobic and van der Waals interactions. Several hotspots and non-hotspots corresponded to Syt1 residues that are reported to link with Ca ²⁺ -dependent phenomena and membrane vesicular fusion. Solvent-excluded volume (V _SE ) analyses showed that local hotspot burial and global desolvation together influenced protein adaptability, distinguishing ligands with similar hotspots but divergent thermodynamics. Linear correlations established ΔG _binding as an indicator of hydrophobic hotspot burial. KPCA-IG identified ΔV _SE and RMSD as dominant mechanistic features capturing cooperative induced-fit responses. Pyridoxal phosphate, melatonin and triiodothyronine emerged as mechanistically top adaptive binders, involving adjustable desolvation, hotspot expansion, optimal flexibility and pose retention. Methyltetrahydrofolate and cortisol showed intermediate burial-dominated cooperativity. Argireline, thiamine and estradiol exhibited ligand-specific burial-dominated, pose-locked and adaptability-limited bindings. Overall, our study demonstrates that desolvation control, induced-fit adaptability and structural flexibility, not ΔG _binding alone, govern Syt1-ligand binding, revealing Syt1 as a versatile presynaptic target for modulation by these bioactive molecules. Graphical Insight Box: In silico studies using molecular docking and molecular dynamics simulations show, for the first time, that the Synaptotagmin-1 (Syt1) C2AB domain binds spontaneously and favourably to vitamins (B1, B6, and active-form B9) and hormones (cortisol, estradiol, triiodothyronine, and melatonin) at hotspot-enriched C2A, C2B, and inter-domain interfaces. Hotspot energetic decomposition, solvent-excluded volume, and RMSD analyses indicate that ligand recognition is governed by cooperative hydrophobic van der Waals packing, desolvation-driven solvent reorganization, and conformational adaptability. Kernel-based nonlinear multivariate ranking reveals that induced-fit flexibility and solvent reorganization are key determinants of productive binding, distinguishing adaptive, passive, and pose-locked binding modes. This thermodynamic-structural-solvation framework indicates that bioactive ligand binding to Syt1 may modulate calcium-dependent vesicle fusion and guide presynaptic drug discovery.

Alzheimer’s disease (AD) poses a longstanding health challenge, prompting a century-long exploration into its etiology and progression. Despite significant advancements in medical science, current AD treatments provide only symptomatic relief, urging a shift towards innovative paradigms. This study, departing from the amyloid hypothesis, integrates Systems Pharmacology, Molecular Docking and Molecular Dynamic Simulations to investigate a polyherbal phytoformulation (US 7,273,626 B2) rooted in Ayurveda for AD, consisting of Bacopa monnieri, Hippophae rhamnoides, and Dioscorea bulbifera (BHD). Diosgenin emerges as a crucial compound, aligning with previous studies, yet recognizing its limitations in explaining BHD’s mechanism, this research delves into the intricate network of interactions. Protein-Protein Interaction (PPI) network analysis identifies hub genes (ALOX5, GSK3B, ACHE, SRC, AKT1, EGFR, PIK3R1, ESR1 and APP), suggesting a systems-level modulation of AD. Enrichment analyses unveil 370 AD-associated genes and key terms like “Cellular Response to Chemical Stimulus” and “Regulation of Biological Quality.” KEGG pathway analysis underscores BHD’s potential in Alzheimer’s disease pathway (hsa05010), Endocrine resistance (hsa01522), and PI3K-Akt signaling (hsa04151). Molecular docking, carefully selecting compounds (Kaempferol, Quercetin, Myricetin, Isorhamnetin, Beta-Sitosterol, Stigmasterol, Emodin and Diosgenin) and top modulated targets, validates interactions with high dock scores, providing promising therapeutic avenues. Two core targets, Acetylcholinesterase (AChE) and Estrogen Receptor 1 (ESR1), were identified for further investigation due to their critical roles in Alzheimer’s disease. To validate the molecular docking results, Molecular Dynamics (MD) simulations were performed on the AChE complexes with Myricetin, Beta-Sitosterol, and Stigmasterol, as well as the ESR1 complexes with Emodin, Diosgenin, and Beta-Sitosterol. These simulations were then compared to the interactions observed with the marketed drugs Donepezil and Estradiol, which are commonly used in Alzheimer’s treatment. The MD simulations provided detailed insights into the stability and behavior of these complexes over time. The findings indicated that Myricetin and Emodin not only maintained stable interactions with AChE and ESR1 but also exhibited greater stability than Donepezil and Estradiol at specific time points and protein regions, as demonstrated by lower RMSD and RMSF values. These results suggest that natural compounds hold promise as potential therapeutic agents in the treatment of Alzheimer’s disease, offering new avenues for drug development, while the formulation BHD shows potential as an adjuvant in integrative medicine alongside standard Alzheimer’s treatments, effectively targeting related pathways and genes.

Papillomaviruses (PVs) are causative agents for warts and cancers in different parts of the body in the mammalian lineage. Therefore, these viruses are proposed as model organisms to study host immune responses to pathogens causing chronic infections. The virus-associated cancer progression depends on two integral processes namely angiogenesis and immune response (AIR). The angiogenesis process aids in tumour progression through vessel formation and maturation but the host immune response, in contrast, makes every attempt to eliminate pathogens and thereby maintain healthy tissues. However, the evolutionary contribution of individual viral genes and host AIR genes in carcinogenesis is yet to be explored. Here, we applied the evolutionary genomics approach to find correlated evolution between six PV genes and 23 host AIR-related genes. We estimated that interferon (IFN)-γ is the only host gene evolving in a correlated manner with all six PV genes under study. Furthermore, three PV genes, such as L2, E6 and E7, are found to interact with two thirds of host AIR-related genes. Moreover, a combined differential gene expression analysis and network analysis showed that inflammatory cytokine IFN-γ is the key regulator of hub genes in the protein–protein interaction network of the differentially expressed genes. Functional enrichment of these hub genes is consistent with their established role in different cancers and viral infections. Overall, we conclude that IFN-γ maintains selective pressure on mammalian PV genes and seems to be a potential biomarker for PV-related cancers. This study demonstrates the evolutionary importance of IFN-γ in deciding the fate of carcinogenic PV variants.

Introduction. Chronic persistent device-related infections (DRIs) often give culture-negative results in a microbiological investigation. In such cases, investigations on the device metagenome might have a diagnostic value. Materials and Methods. The 16SrRNA gene sequence analysis and next-generation sequencing (NGS) of clinical metagenome were performed to detect bacterial diversity on invasive medical devices possibly involved in culture-negative DRIs. Device samples were first subjected to microbiological investigation followed by metagenome analysis. Environmental DNA (e-DNA) isolated from device samples was subjected to 16SrRNA gene amplification followed by Sanger sequencing (n=14). In addition, NGS of the device metagenome was also performed (n=12). Five samples were only common in both methods. Results. Microbial growth was observed in only nine cases; among these, five cases were considered significant growth, and in the remaining four cases, growth was considered either insignificant or contaminated. Culture and sequencing analysis yielded identical results only in six cases. In culture-negative cases, Sanger sequencing of 16SrRNA gene and NGS of 16SrDNA microbiome was able to identify the presence of rarely described human pathogens, namely Streptococcus infantis, Gemella haemolysans, Meiothermus silvanus, Schlegelella aquatica, Rothia mucilaginosa, Serratia nematodiphila, and Enterobacter asburiae, along with some known common nosocomial pathogens. Bacterial species such as M. silvanus and S. nematodiphila that are never reported in human infection were also identified. Conclusions. Results of a small number of diverse samples of this pilot study might lead to a path to study a large number of device samples that may validate the diversity witnessed. The study shows that a culture free, a holistic metagenomic approach using NGS could help identify the pathogens in culture-negative chronic DRIs.

‘Tripartite network’ (TN) and ‘combined gene network’ (CGN) were constructed and their hub-bottleneck and driver nodes (44 genes) were evaluated as ‘target genes’ (TG) to identify 21 ‘candidate genes’ (CG) and their relationship with neurological manifestations of COVID-19. TN was developed using neurological symptoms of COVID-19 found in literature. Under query genes (TG of TN), co-expressed genes were identified using pair-wise mutual information to genes available in RNA-Seq autopsy data of frontal cortex of COVID-19 victims. CGN was constructed with genes selected from TN and co-expressed in COVID-19. TG and their connecting genes of respective networks underwent functional analyses through findings of their enrichment terms and pair-wise ‘semantic similarity scores’ (SSS). A new integrated ‘weighted harmonic mean score’ was formulated assimilating values of SSS and STRING-based ‘combined score’ of the selected TG-pairs, which provided CG-pairs with properties of CGs as co-expressed and ‘indispensable nodes’ in CGN. Finally, six pairs sharing seven ‘prevalent CGs’ (ADAM10, ADAM17, AKT1, CTNNB1, ESR1, PIK3CA, FGFR1) showed linkages with the phenotypes (a) directly under neurodegeneration, neurodevelopmental diseases, tumour/cancer and cellular signalling, and (b) indirectly through other CGs under behavioural/cognitive and motor dysfunctions. The pathophysiology of ‘prevalent CGs’ has been discussed to interpret neurological phenotypes of COVID-19.

Human papillomavirus (HPV) infections often show no symptoms but sometimes lead to either warts or carcinoma based on the HPV genotype. The relationship between HPV infections and cervical cancer have been well studied in the past two decades. However, distinguishing carcinogenic HPV variants from non-carcinogenic ones remains a major challenge in clinical genetic testing of HPV-induced cancer samples. All of the published HPV carcinogenicity prediction methods are neither publically available nor tested with two-thirds of available HPV variants. The nucleotide composition-based studies are the simplest and most precise methods of characterizing new genomes. Hence, there is a need for machine learning models which can predict the carcinogenic nature of newly discovered HPV based on their genomic composition. We developed a standalone and web tool, CarcinoHPVPred (h t t p :// test5.bicpu.edu.in/CarcinoHPVPred.php), for predicting the phenotype of HPV with a range of a high accuracy between 94% - 100%. This tool consists of machine learning models build upon genomic features of two genes namely E2 and E6. Overall, the accurate and early prediction of carcinogenic nature of HPV can be performed with this only available tool of its kind till date.

Introduction and Aim: Aging alters limbic structure, sex and hemispheric variations with anterior-posterior dominances of which are still obscure in relation to cognition in humans.   Methodology: The present study included MRI coronal images for measurements of volumes [hippocampal volume (HV), amygdala volume (AV)], shapes [hippocampal angle (HA), medial distance ratio (MDR), parahippocampal angle (PHA)] and their hemispheric asymmetry indices (AI) in male and female healthy individuals of young and aged groups. The best-fitted formulae of 'volume index' (VI) and 'shape index' (SI) were evaluated for better interpretation.   Results: In young, males showed larger bi-hemispheric volumes and AV/HV-ratios, greater HA and MDR values with distinct hemispheric dominances and greater VI with different SI values, compared to females. Aged subjects showed lesser VI and bi-hemispheric volumes compared to young counterparts where the decrement of volumes were more in aged males resulting in same volumes and AV/HV-ratios in both aged sexes. Only aged females exhibited greater HA and MDR values with altered SI compared to young counterparts retaining the sex-specific such differences with their altered AIs. The parametric changes showed significant correlations in respective cases. PHA remained unaltered in all cases.   Conclusion: Sex-specific volume differences in young and volume reduction in age might associate with distinct alterations of hippocampal shapes viz. shifting of the (a) head (anterior portion) vertically compared to its horizontal position and (b) body (posterior portion) laterally toward temporal cortex, in both hemispheres. The recent findings claim further studies to unveil the cognitive variations corroborated with such limbic structural/shape alterations among sexes and ages.