Investigation into in silico and in vitro approaches for inhibitors targeting MCM10 in Leishmania donovani: a comprehensive study.

Visceral Leishmaniasis (VL), the second neglected tropical disease caused by various Leishmania species, presents a significant public health challenge due to limited treatment options and the absence of vaccines. The agent responsible for visceral leishmaniasis, also referred to as "black fever" in India, is Leishmania donovani. This study focuses on L. donovani Minichromosome maintenance 10 (LdMcm10), a crucial protein in the DNA replication machinery, as a potential therapeutic target in Leishmania therapy using in silico and in vitro approaches. We employed bioinformatics tools, molecular docking, and molecular dynamics simulations to predict potential inhibitors against the target protein. The research revealed that the target protein lacks homologues in the host, emphasizing its potential as a drug target. Ligands from the DrugBank database were screened against LdMcm10 using PyRx software. The top three compounds, namely suramin, vapreotide, and pasireotide, exhibiting the best docking scores, underwent further investigation through molecular dynamic simulation and in vitro analysis. The observed structural dynamics suggested that LdMcm10-ligand complexes maintained consistent binding throughout the 300 ns simulation period, with minimal variations in their backbone. These findings suggest that these three compounds hold promise as potential lead compounds for developing new drugs against leishmaniasis. In vitro experiments also demonstrated a dose-dependent reduction in L. donovani viability for suramin, vapreotide, and pasireotide, with computed IC50 values providing quantitative metrics of their anti-leishmanial efficacy. The research offers a comprehensive understanding of LdMcm10 as a drug target and provides a foundation for further investigations and clinical exploration, ultimately advancing drug discovery strategies for leishmaniasis treatment.

Identification of potential inhibitor against Leishmania donovani mitochondrial DNA primase through in-silico and in vitro drug repurposing approaches.

Leishmania donovani is the causal organism of leishmaniasis with critical health implications affecting about 12 million people around the globe. Due to less efficacy, adverse side effects, and resistance, the available therapeutic molecules fail to control leishmaniasis. The mitochondrial primase of Leishmania donovani (LdmtPRI1) is a vital cog in the DNA replication mechanism, as the enzyme initiates the replication of the mitochondrial genome of Leishmania donovani. Hence, we target this protein as a probable drug target against leishmaniasis. The de-novo approach enabled computational prediction of the three-dimensional structure of LdmtPRI1, and its active sites were identified. Ligands from commercially available drug compounds were selected and docked against LdmtPRI1. The compounds were chosen for pharmacokinetic study and molecular dynamics simulation based on their binding energies and protein interactions. The LdmtPRI1 gene was cloned, overexpressed, and purified, and a primase activity assay was performed. The selected compounds were verified experimentally by the parasite and primase inhibition assay. Capecitabine was observed to be effective against the promastigote form of Leishmania donovani, as well as inhibiting primase activity. This study's findings suggest capecitabine might be a potential anti-leishmanial drug candidate after adequate further studies.

An integrated Bayesian framework for multi-omics prediction and classification.

With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.

Sustained remissions in CLL after frontline FCR treatment with very-long-term follow-up.

Chemoimmunotherapy with fludarabine, cyclophosphamide, and rituximab (FCR) achieves durable remissions, with flattening of the progression-free survival (PFS) curve in patients with mutated immunoglobulin heavy chain variable gene (IGHV-M). We updated long-term follow-up results from the original 300-patient FCR study initiated at MD Anderson in 1999. The current median follow-up is 19.0 years. With this extended follow-up, the median PFS for patients with IGHV-M was 14.6 years vs 4.2 years for patients with unmutated IGHV (IGHV-UM). Disease progression beyond 10 years was uncommon. In total, 16 of 94 (17%) patients in remission at 10 years subsequently progressed with the additional follow-up compared with the patients in our prior report in 2015. Only 4 of 45 patients (9%) with IGHV-M progressed beyond 10 years. Excluding Richter transformation, 96 of 300 patients (32%) developed 106 other malignancies, with 19 of 300 (6.3%) developing therapy-related myeloid neoplasms (tMNs), which were fatal in 16 of 19 (84%). No pretreatment patient characteristics predicted the risk of tMNs. In summary, FCR remains an option for patients with IGHV-M chronic lymphocytic leukemia (CLL), with a significant fraction achieving functional cure of CLL. A risk-benefit assessment is warranted when counseling patients, balancing potential functional cure with the risk of late relapses and serious secondary malignancies.

Synthesis and biological evaluation of carbohydrate-Coumarin/vanillic acid hybrid as a promising antiparasitic agent.

Leishmaniasis is caused by infection with the protozoan parasites Leishmania. It is classified as one of the most significant neglected tropical diseases. It remains a significant global public health concern. Current treatments include the use of pentavalent antimonial, amphotericin B, pentamidine, miltefosine, and paromomycin. However, several limitations such as toxicity, side effect, and resistance to these drugs of certain species are of concern. To combat this disease, effective chemotherapy is urgently required for its treatment and management. In this study, we synthesized a series of carbohydrate-coumarin/vanillic acid hybrids linked through triazole moiety via CuACC (Copper-catalysed azide-alkyne cycloaddition) reaction. These compounds were evaluated for their in vitro antiparasitic activity using MTT assay against Leishmania donovani whereas, all compounds show IC50 value in the range of 65-74 µM.

Kernelized multiview signed graph learning for single-cell RNA sequencing data.

BACKGROUND: Characterizing the topology of gene regulatory networks (GRNs) is a fundamental problem in systems biology. The advent of single cell technologies has made it possible to construct GRNs at finer resolutions than bulk and microarray datasets. However, cellular heterogeneity and sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing GRNs. Additionally, most GRN reconstruction approaches estimate a single network for the entire data. This could cause potential loss of information when single cell datasets are generated from multiple treatment conditions/disease states. RESULTS: To better characterize single cell GRNs under different but related conditions, we propose the joint estimation of multiple networks using multiple signed graph learning (scMSGL). The proposed method is based on recently developed graph signal processing (GSP) based graph learning, where GRNs and gene expressions are modeled as signed graphs and graph signals, respectively. scMSGL learns multiple GRNs by optimizing the total variation of gene expressions with respect to GRNs while ensuring that the learned GRNs are similar to each other through regularization with respect to a learned signed consensus graph. We further kernelize scMSGL with the kernel selected to suit the structure of single cell data. CONCLUSIONS: scMSGL is shown to have superior performance over existing state of the art methods in GRN recovery on simulated datasets. Furthermore, scMSGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.

Single-cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) dose-dependently induces the development of hepatic fat accumulation and inflammation with fibrosis in mice initially in the portal region. Conversely, differential gene and protein expression is first detected in the central region. To further investigate cell-specific and spatially resolved dose-dependent changes in gene expression elicited by TCDD, single-nuclei RNA sequencing and spatial transcriptomics were used for livers of male mice gavaged with TCDD every 4 days for 28 days. The proportion of 11 cell (sub)types across 131 613 nuclei dose-dependently changed with 68% of all portal and central hepatocyte nuclei in control mice being overtaken by macrophages following TCDD treatment. We identified 368 (portal fibroblasts) to 1339 (macrophages) differentially expressed genes. Spatial analyses revealed initial loss of portal identity that eventually spanned the entire liver lobule with increasing dose. Induction of R-spondin 3 (Rspo3) and pericentral Apc, suggested dysregulation of the Wnt/ß-catenin signaling cascade in zonally resolved steatosis. Collectively, the integrated results suggest disruption of zonation contributes to the pattern of TCDD-elicited NAFLD pathologies.

Drug repurposing based novel anti-leishmanial drug screening using in-silico and in-vitro approaches.

Visceral leishmaniasis is a neglected tropical disease and is mainly caused by L. donovani in the Indian subcontinent. The mitochondria genome replication in Leishmania spp. is having a very specific mechanism, and it is initiated by a key enzyme called mitochondrial primase. This enzyme is essential for the onset of the replication process and growth of the parasite. Therefore, we focused on the primase protein as a potential therapeutic target for combating leishmaniasis diseases. We started our studies molecular modeling and followed by docking of the FDA-approved drug library into the binding site of the primase protein. The top 30 selected compounds were subjected for molecular dynamics studies. Also, the target protein was cloned, purified, and tested experimentally (primase activity assays and inhibition assays). Some compounds were very effective against the Leishmania cell culture. All these approaches helped us to identify few possible novel anti-leishmanial drugs such as Pioglitazone and Mupirocin. These drugs are effectively involved in inhibiting the promastigote of L. donovani, and it can be utilized in the next level of clinical trials. Communicated by Ramaswamy H. Sarma.

scSGL: kernelized signed graph learning for single-cell gene regulatory network inference.

MOTIVATION: Elucidating the topology of gene regulatory networks (GRNs) from large single-cell RNA sequencing datasets, while effectively capturing its inherent cell-cycle heterogeneity and dropouts, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, a characteristic feature of GRNs, which are capable of accounting for both activating and inhibitory relationships in the gene network. They are also incapable of handling high proportion of zero values present in the single cell datasets. RESULTS: To this end, we propose a novel signed GL approach, scSGL, that learns GRNs based on the assumption of smoothness and non-smoothness of gene expressions over activating and inhibitory edges, respectively. scSGL is then extended with kernels to account for non-linearity of co-expression and for effective handling of highly occurring zero values. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework. Performance assessment using simulated datasets demonstrates the superior performance of kernelized scSGL over existing state of the art methods in GRN recovery. The performance of scSGL is further investigated using human and mouse embryonic datasets. AVAILABILITY AND IMPLEMENTATION: The scSGL code and analysis scripts are available on https://github.com/Single-Cell-Graph-Learning/scSGL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Distinct Potentially Adaptive Accumulation of Truncation Mutations in Salmonella enterica serovar Typhi and Salmonella enterica serovar Paratyphi A.

Gene inactivation through the accumulation of truncation (or premature stop codon) mutations is a common mode of evolution in bacteria. It is frequently believed to result from reductive evolutionary processes allowing purging of superfluous traits. However, several works have demonstrated that, similar to the occurrences of inactivating nonsynonymous (i.e., amino acid replacement) mutations under positive selection pressures, truncation mutations can also be adaptive where specific traits deleterious in particular environmental conditions need to be inactivated for survival. Here, we performed a comparative analysis of genome-wide accumulation of truncation mutations in Salmonella enterica serovar Typhi and Salmonella enterica serovar Paratyphi A. Considering the known convergent evolutionary trajectories in these two serovars, we expected a strong overlap of truncated genes in S. Typhi and S. Paratyphi A, emerging through either reductive or adaptive dynamics. However, we detected a distinct set of core truncated genes encoding different overrepresented functional clusters in each serovar. In 54% and 28% truncated genes in S. Typhi and S. Paratyphi A, respectively, inactivating mutations were acquired by only different subsets of isolates, instead of all isolates analyzed for that serovar. Importantly, 62% truncated genes (P < 0.001) in S. Typhi and S. Paratyphi A were also targeted by convergent amino acid mutations in different serovars, suggesting those genes to be under selection pressures. Our findings indicate significant presence of potentially adaptive truncation mutations in conjunction with the ones emerging due to reductive evolution. Further experimental and large-scale bioinformatic studies are necessary to better explore the impact of such adaptive footprints of truncation mutations in the evolution of bacterial virulence. IMPORTANCE Detecting the adaptive mutations leading to gene inactivation or loss of function is crucial for understanding their contribution in the evolution of bacterial virulence and antibiotic resistance. Such inactivating mutations, apart from being of nonsynonymous (i.e., amino acid replacement) nature, can also be truncation mutations, abruptly trimming the length of encoded proteins. Importantly, the notion of reductive evolutionary dynamics is primarily accepted toward the accumulation of truncation mutations. However, our case study on S. Typhi and S. Paratyphi A, two human-restricted systemically invasive pathogens exerting similar clinical manifestations, indicated that a significant proportion of truncation mutations emerge from positive selection pressures. The candidate genes from our study will enable directed functional assays for deciphering the adaptive role of truncation mutations in S. Typhi and S. Paratyphi A pathogenesis. Also, our genome-level analytical approach will pave the way to understand the contribution of truncation mutations in the adaptive evolution of other bacterial pathogens.

Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose-response study designs.

The application of single-cell RNA sequencing (scRNAseq) for the evaluation of chemicals, drugs, and food contaminants presents the opportunity to consider cellular heterogeneity in pharmacological and toxicological responses. Current differential gene expression analysis (DGEA) methods focus primarily on two group comparisons, not multi-group dose-response study designs used in safety assessments. To benchmark DGEA methods for dose-response scRNAseq experiments, we proposed a multiplicity corrected Bayesian testing approach and compare it against 8 other methods including two frequentist fit-for-purpose tests using simulated and experimental data. Our Bayesian test method outperformed all other tests for a broad range of accuracy metrics including control of false positive error rates. Most notable, the fit-for-purpose and standard multiple group DGEA methods were superior to the two group scRNAseq methods for dose-response study designs. Collectively, our benchmarking of DGEA methods demonstrates the importance in considering study design when determining the most appropriate test methods.

In-silico studies on Myo inositol-1-phosphate synthase of Leishmania donovani in search of anti-leishmaniasis.

Myo-inositol is one of the vital nutritional requirements for the Leishmania parasites' survival and virulence in the mammalian host. . Myo-inositol-1-phosphate synthase (MIPS) is responsible for the synthesis of myo-inositol in Leishmania, which plays a vital role in Leishmania's virulence to mammalian hosts. Earlier studies suggest MIP synthase as a potential drug target against which valproate was used as a drug. So, MIP synthase can be used as a target for anti-leishmanial drugs, and its inhibition may help in preventing leishmaniasis. The present study aims to identify valproate's potent analogs as drugs against MIP synthase of L. donovani (Ld-MIPS) with minimum side effects and toxicity to host.In this study, the three-dimensional structure of Ld-MIPS was built, followed by active site prediction. Ligand-based virtual screening was done using hybrid similarity recognition methods. The best 123 valproate analogs were filtered based on their quantitative structure activity relationship (QSAR) properties and were docked against Ld-MIPS using FlexX, PyRx and iGEMDOCK software. The topmost five ligands were selected for molecular dynamics simulation and pharmacokinetic analysis based on the docking score. Simulation studies up to 30 ns revealed that all five lead molecules bound with Ld-MIPS throughout MD simulation and there was no variation in their backbone. All the chosen inhibitors exhibited good pharmacokinetics/ADMET predictions with an excellent absorption profile, metabolism, oral bioavailability, solubility, excretion, and minimal toxicity, suggesting that these inhibitors may further be developed as anti-leishmaniasis drugs to prevent the spread of leishmaniasis.Communicated by Ramaswamy H. Sarma.

Discovering Potential RNA Dependent RNA Polymerase Inhibitors as Prospective Drugs Against COVID-19: An in silico Approach.

COVID-19, caused by Severe Acute Respiratory Syndrome Corona Virus 2, is declared a Global Pandemic by WHO in early 2020. In the present situation, though more than 180 vaccine candidates with some already approved for emergency use, are currently in development against SARS-CoV-2, their safety and efficacy data is still in a very preliminary stage to recognize them as a new treatment, which demands an utmost emergency for the development of an alternative anti-COVID-19 drug sine qua non for a COVID-19 free world. Since RNA-dependent RNA polymerase (RdRp) is an essential protein involved in replicating the virus, it can be held as a potential drug target. We were keen to explore the plant-based product against RdRp and analyze its inhibitory potential to treat COVID-19. A unique collection of 248 plant compounds were selected based on their antiviral activity published in previous literature and were subjected to molecular docking analysis against the catalytic sub-unit of RdRp. The docking study was followed by a pharmacokinetics analysis and molecular dynamics simulation study of the selected best-docked compounds. Tellimagrandin I, SaikosaponinB2, Hesperidin and (-)-Epigallocatechin Gallate were the most prominent ones that showed strong binding affinity toward RdRp. All the compounds mentioned showed satisfactory pharmacokinetics properties and remained stabilized at their respective binding sites during the Molecular dynamics simulation. Additionally, we calculated the free-binding energy/the binding properties of RdRp-ligand complexes with the connection of MM/GBSA. Interestingly, we observe that SaikosaponinB2 gives the best binding affinity (∆Gbinding = -42.43 kcal/mol) in the MM/GBSA assay. Whereas, least activity is observed for Hesperidin (∆Gbinding = -22.72 kcal/mol). Overall our study unveiled the feasibility of the SaikosaponinB2 to serve as potential molecules for developing an effective therapy against COVID-19 by inhibiting one of its most crucial replication proteins, RdRp.