Transcriptomics Demonstrates Significant Biological Effect of Growing Stem Cells on RGD-Cotton Scaffold.

Stem cell therapy provides a viable alternative treatment for degenerated or damaged tissue. Stem cells have been used either alone or in conjunction with an artificial scaffold. The latter provides a structural advantage by enabling the cells to thrive in three-dimensional (3D) settings, closely resembling the natural in vivo environments. Previously, we disclosed the development of a 3D scaffold made from cotton, which was conjugated with arginyl-glycyl-aspartic acid (RGD), to facilitate the growth and proliferation of mesenchymal stem cells (MSCs). This scaffold allowed the MSCs to adhere and proliferate without compromising their viability or their stem cell markers. A comprehensive analysis investigation of the molecular changes occurring in MSCs adhering to the cotton fibers will contribute to the advancement of therapy. The objective of this study is to analyze the molecular processes occurring in the growth of MSCs on a cotton-RGD conjugated-based scaffold by examining their gene expression profiles. To achieve this, we conducted an experiment where MSCs were seeded with and without the scaffold for a duration of 48 h. Subsequently, cells were collected for RNA extraction, cDNA synthesis, and whole-transcriptomic analysis performed on both populations. Our analysis revealed several upregulated and downregulated differently expressed genes in the MSCs adhering to the scaffold compared with the control cells. Through gene ontology analysis, we were able to identify enriched biological processes, molecular functions, pathways, and protein-protein interactions in these differentially expressed genes. Our data suggest that the scaffold may have the potential to enhance osteogenesis in the MSCs. Furthermore, our results indicate that the scaffold does not induce oxidative stress, inflammation, or aging in the MSCs. These findings provide valuable insights for the application of MSCs in tissue engineering and regenerative medicine.

Computational insights into the stereo-selectivity of catechins for the inhibition of the cancer therapeutic target EGFR kinase.

The epidermal growth factor receptor (EGFR) plays a crucial role in regulating cellular growth and survival, and its dysregulation is implicated in various cancers, making it a prime target for cancer therapy. Natural compounds known as catechins have garnered attention as promising anticancer agents. These compounds exert their anticancer effects through diverse mechanisms, primarily by inhibiting receptor tyrosine kinases (RTKs), a protein family that includes the notable member EGFR. Catechins, characterized by two chiral centers and stereoisomerism, demonstrate variations in chemical and physical properties due to differences in the spatial orientation of atoms. Although previous studies have explored the membrane fluidity effects and transport across cellular membranes, the stereo-selectivity of catechins concerning EGFR kinase inhibition remains unexplored. In this study, we investigated the stereo-selectivity of catechins in inhibiting EGFR kinase, both in its wild-type and in the prevalent L858R mutant. Computational analyses indicated that all stereoisomers, including the extensively studied catechin (-)-EGCG, effectively bound within the ATP-binding site, potentially inhibiting EGFR kinase activity. Notably, gallated catechins emerged as superior EGFR inhibitors to their non-gallated counterparts, revealing intriguing binding trends. The top four stereoisomers exhibiting high dock scores and binding energies with wild-type EGFR comprise (-)-CG (-)-GCG (+)-CG, and (-)-EGCG. To assess dynamic behavior and stability, molecular dynamics simulations over 100 ns were conducted for the top-ranked catechin (-)-CG and the widely investigated catechin (-)-EGCG with EGFR kinase. This study enhances our understanding of how the stereoisomeric nature of a drug influences inhibitory potential, providing insights that could guide the selection of specific stereoisomers for improved efficacy inexisting drugs.

A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome-Interactome Signature for Predicting Non-Small Cell Lung Cancer.

The lack of precise molecular signatures limits the early diagnosis of non-small cell lung cancer (NSCLC). The present study used gene expression data and interaction networks to develop a highly accurate model with the least absolute shrinkage and selection operator (LASSO) for predicting NSCLC. The differentially expressed genes (DEGs) were identified in NSCLC compared with normal tissues using TCGA and GTEx data. A biological network was constructed using DEGs, and the top 20 upregulated and 20 downregulated hub genes were identified. These hub genes were used to identify signature genes with penalized logistic regression using the LASSO to predict NSCLC. Our model's development involved the following steps: (i) the dataset was divided into 80% for training (TR) and 20% for testing (TD1); (ii) a LASSO logistic regression analysis was performed on the TR with 10-fold cross-validation and identified a combination of 17 genes as NSCLC predictors, which were used further for development of the LASSO model. The model's performance was assessed on the TD1 dataset and achieved an accuracy and an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.986 and 0.998, respectively. Furthermore, the performance of the LASSO model was evaluated using three independent NSCLC test datasets (GSE18842, GSE27262, GSE19804) and achieved high accuracy, with an AUC-ROC of >0.99, >0.99, and 0.95, respectively. Based on this study, a web application called NSCLCpred was developed to predict NSCLC.

Novel tumour suppressor roles for GZMA and RASGRP1 in Theileria annulata-transformed macrophages and human B lymphoma cells.

Theileria annulata is a tick-transmitted apicomplexan parasite that infects and transforms bovine leukocytes into disseminating tumours that cause a disease called tropical theileriosis. Using comparative transcriptomics we identified genes transcriptionally perturbed during Theileria-induced leukocyte transformation. Dataset comparisons highlighted a small set of genes associated with Theileria-transformed leukocyte dissemination. The roles of Granzyme A (GZMA) and RAS guanyl-releasing protein 1 (RASGRP1) were verified by CRISPR/Cas9-mediated knockdown. Knocking down expression of GZMA and RASGRP1 in attenuated macrophages led to a regain in their dissemination in Rag2/γC mice confirming their role as dissemination suppressors in vivo. We further evaluated the roles of GZMA and RASGRP1 in human B lymphomas by comparing the transcriptome of 934 human cancer cell lines to that of Theileria-transformed bovine host cells. We confirmed dampened dissemination potential of human B lymphomas that overexpress GZMA and RASGRP1. Our results provide evidence that GZMA and RASGRP1 have a novel tumour suppressor function in both T. annulata-infected bovine host leukocytes and in human B lymphomas.

The Impact of Natural Antioxidants on the Regenerative Potential of Vascular Cells.

With advances in technology, the impact of natural antioxidants on vascular cell regeneration is attracting enormous attention as many current studies are now exploring the clinical potential of antioxidants in regenerative medicine. Natural antioxidants are an important step for improving future treatment and prevention of various diseases such as cardiovascular, cancer, neurodegenerative, and diabetes. The use of natural antioxidants which have effects on several types of stem cells with the potential to differentiate into functional endothelium and smooth muscle cells (known as vascular progenitors) for vascular regeneration might override pharmaceutical and surgical treatments. The natural antioxidant systems comprise of several components present in fruits, vegetables, legumes, medicinal plants, and other animal-derived products that interact with reactive free radicals such as oxygen and nitrogen species to neutralize their oxidative damaging effects on vascular cells. Neutralization by antioxidants involves the breaking down of the oxidative cascade chain reactions in the cell membranes in order to fine-tune the free radical levels. The effect of natural antioxidants on vascular regeneration includes restoration or establishment of new vascular structures and functions. In this review, we highlight the significant effects of natural antioxidants on modulating vascular cells to regenerate vessels, as well as possible mechanisms of action and the potential therapeutic benefits on health. The role of antioxidants in regenerating vessels may be critical for the future of regenerative medicine in terms of the maintenance of the normal functioning of vessels and the prevention of multiple vascular diseases.

miR-126-5p by direct targeting of JNK-interacting protein-2 (JIP-2) plays a key role in Theileria-infected macrophage virulence.

Theileria annulata is an apicomplexan parasite that infects and transforms bovine macrophages that disseminate throughout the animal causing a leukaemia-like disease called tropical theileriosis. Using deep RNAseq of T. annulata-infected B cells and macrophages we identify a set of microRNAs induced by infection, whose expression diminishes upon loss of the hyper-disseminating phenotype of virulent transformed macrophages. We describe how infection-induced upregulation of miR-126-5p ablates JIP-2 expression to release cytosolic JNK to translocate to the nucleus and trans-activate AP-1-driven transcription of mmp9 to promote tumour dissemination. In non-disseminating attenuated macrophages miR-126-5p levels drop, JIP-2 levels increase, JNK1 is retained in the cytosol leading to decreased c-Jun phosphorylation and dampened AP-1-driven mmp9 transcription. We show that variation in miR-126-5p levels depends on the tyrosine phosphorylation status of AGO2 that is regulated by Grb2-recruitment of PTP1B. In attenuated macrophages Grb2 levels drop resulting in less PTP1B recruitment, greater AGO2 phosphorylation, less miR-126-5p associated with AGO2 and a consequent rise in JIP-2 levels. Changes in miR-126-5p levels therefore, underpin both the virulent hyper-dissemination and the attenuated dissemination of T. annulata-infected macrophages.

In silico analysis to identify vaccine candidates common to multiple serotypes of Shigella and evaluation of their immunogenicity.

Shigellosis or bacillary dysentery is an important cause of diarrhea, with the majority of the cases occurring in developing countries. Considering the high disease burden, increasing antibiotic resistance, serotype-specific immunity and the post-infectious sequelae associated with shigellosis, there is a pressing need of an effective vaccine against multiple serotypes of the pathogen. In the present study, we used bio-informatics approach to identify antigens shared among multiple serotypes of Shigella spp. This approach led to the identification of many immunogenic peptides. The five most promising peptides based on MHC binding efficiency were a putative lipoprotein (EL PGI I), a putative heat shock protein (EL PGI II), Spa32 (EL PGI III), IcsB (EL PGI IV) and a hypothetical protein (EL PGI V). These peptides were synthesized and the immunogenicity was evaluated in BALB/c mice by ELISA and cytokine assays. The putative heat shock protein (HSP) and the hypothetical protein elicited good humoral response, whereas putative lipoprotein, Spa32 and IcsB elicited good T-cell response as revealed by increased IFN-γ and TNF-α cytokine levels. The patient sera from confirmed cases of shigellosis were also evaluated for the presence of peptide specific antibodies with significant IgG and IgA antibodies against the HSP and the hypothetical protein, bestowing them as potential future vaccine candidates. The antigens reported in this study are novel and have not been tested as vaccine candidates against Shigella. This study offers time and cost-effective way of identifying unprecedented immunogenic antigens to be used as potential vaccine candidates. Moreover, this approach should easily be extendable to find new potential vaccine candidates for other pathogenic bacteria.

In silico screening for candidate chassis strains of free fatty acid-producing cyanobacteria.

BACKGROUND: Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of production of FFA to be commercially viable; thus new chassis strains that require less engineering are needed. Although more than 120 cyanobacterial genomes are sequenced, the natural potential of these strains for FFA production and excretion has not been systematically estimated. RESULTS: Here we present the FFA SC (FFASC), an in silico screening method that evaluates the potential for FFA production and excretion of cyanobacterial strains based on their proteomes. A literature search allowed for the compilation of 64 proteins, most of which influence FFA production and a few of which affect FFA excretion. The proteins are classified into 49 orthologous groups (OGs) that helped create rules used in the scoring/ranking of algorithms developed to estimate the potential for FFA production and excretion of an organism. Among 125 cyanobacterial strains, FFASC identified 20 candidate chassis strains that rank in their FFA producing and excreting potential above the specifically engineered reference strain, Synechococcus sp. PCC 7002. We further show that the top ranked cyanobacterial strains are unicellular and primarily include Prochlorococcus (order Prochlorales) and marine Synechococcus (order Chroococcales) that cluster phylogenetically. Moreover, two principal categories of enzymes were shown to influence FFA production the most: those ensuring precursor availability for the biosynthesis of lipids, and those involved in handling the oxidative stress associated to FFA synthesis. CONCLUSION: To our knowledge FFASC is the first in silico method to screen cyanobacteria proteomes for their potential to produce and excrete FFA, as well as the first attempt to parameterize the criteria derived from genetic characteristics that are favorable/non-favorable for this purpose. Thus, FFASC helps focus experimental evaluation only on the most promising cyanobacteria.

Genome-scale comparison of expanded gene families in Plasmodium ovale wallikeri and Plasmodium ovale curtisi with Plasmodium malariae and with other Plasmodium species.

Malaria in humans is caused by six species of Plasmodium parasites, of which the nuclear genome sequences for the two Plasmodium ovale spp., P. ovale curtisi and P. ovale wallikeri, and Plasmodium malariae have not yet been analyzed. Here we present an analysis of the nuclear genome sequences of these three parasites, and describe gene family expansions therein. Plasmodium ovale curtisi and P. ovale wallikeri are genetically distinct but morphologically indistinguishable and have sympatric ranges through the tropics of Africa, Asia and Oceania. Both P. ovale spp. show expansion of the surfin variant gene family, and an amplification of the Plasmodium interspersed repeat (pir) superfamily which results in an approximately 30% increase in genome size. For comparison, we have also analyzed the draft nuclear genome of P. malariae, a malaria parasite causing mild malaria symptoms with a quartan life cycle, long-term chronic infections, and wide geographic distribution. Plasmodium malariae shows only a moderate level of expansion of pir genes, and unique expansions of a highly diverged transmembrane protein family with over 550 members and the gamete P25/27 gene family. The observed diversity in the P. ovale wallikeri and P. ovale curtisi surface antigens, combined with their phylogenetic separation, supports consideration that the two parasites be given species status.

Identification of B-cell epitopes in an antigen for inducing specific class of antibodies.

BACKGROUND: In the past, numerous methods have been developed for predicting antigenic regions or B-cell epitopes that can induce B-cell response. To the best of authors' knowledge, no method has been developed for predicting B-cell epitopes that can induce a specific class of antibody (e.g., IgA, IgG) except allergenic epitopes (IgE). In this study, an attempt has been made to understand the relation between primary sequence of epitopes and the class of antibodies generated. RESULTS: The dataset used in this study has been derived from Immune Epitope Database and consists of 14725 B-cell epitopes that include 11981 IgG, 2341 IgE, 403 IgA specific epitopes and 22835 non-B-cell epitopes. In order to understand the preference of residues or motifs in these epitopes, we computed and compared amino acid and dipeptide composition of IgG, IgE, IgA inducing epitopes and non-B-cell epitopes. Differences in composition profiles of different classes of epitopes were observed, and few residues were found to be preferred. Based on these observations, we developed models for predicting antibody class-specific B-cell epitopes using various features like amino acid composition, dipeptide composition, and binary profiles. Among these, dipeptide composition-based support vector machine model achieved maximum Matthews correlation coefficient of 0.44, 0.70 and 0.45 for IgG, IgE and IgA specific epitopes respectively. All models were developed on experimentally validated non-redundant dataset and evaluated using five-fold cross validation. In addition, the performance of dipeptide-based model was also evaluated on independent dataset. CONCLUSION: Present study utilizes the amino acid sequence information for predicting the tendencies of antigens to induce different classes of antibodies. For the first time, in silico models have been developed for predicting B-cell epitopes, which can induce specific class of antibodies. A web service called IgPred has been developed to serve the scientific community. This server will be useful for researchers working in the field of subunit/epitope/peptide-based vaccines and immunotherapy (http://crdd.osdd.net/raghava/igpred/).

Improved method for linear B-cell epitope prediction using antigen's primary sequence.

One of the major challenges in designing a peptide-based vaccine is the identification of antigenic regions in an antigen that can stimulate B-cell's response, also called B-cell epitopes. In the past, several methods have been developed for the prediction of conformational and linear (or continuous) B-cell epitopes. However, the existing methods for predicting linear B-cell epitopes are far from perfection. In this study, an attempt has been made to develop an improved method for predicting linear B-cell epitopes. We have retrieved experimentally validated B-cell epitopes as well as non B-cell epitopes from Immune Epitope Database and derived two types of datasets called Lbtope_Variable and Lbtope_Fixed length datasets. The Lbtope_Variable dataset contains 14876 B-cell epitope and 23321 non-epitopes of variable length where as Lbtope_Fixed length dataset contains 12063 B-cell epitopes and 20589 non-epitopes of fixed length. We also evaluated the performance of models on above datasets after removing highly identical peptides from the datasets. In addition, we have derived third dataset Lbtope_Confirm having 1042 epitopes and 1795 non-epitopes where each epitope or non-epitope has been experimentally validated in at least two studies. A number of models have been developed to discriminate epitopes and non-epitopes using different machine-learning techniques like Support Vector Machine, and K-Nearest Neighbor. We achieved accuracy from ∼54% to 86% using diverse s features like binary profile, dipeptide composition, AAP (amino acid pair) profile. In this study, for the first time experimentally validated non B-cell epitopes have been used for developing method for predicting linear B-cell epitopes. In previous studies, random peptides have been used as non B-cell epitopes. In order to provide service to scientific community, a web server LBtope has been developed for predicting and designing B-cell epitopes (http://crdd.osdd.net/raghava/lbtope/).

In silico models for B-cell epitope recognition and signaling.

Tremendous technological advances in peptide synthesis and modification in recent years have resolved the major limitations of peptide-based vaccines. B-cell epitopes are major components of these vaccines (besides having other biological applications). Researchers have been developing in silico or computational models for the prediction of both linear and conformational B-cell epitopes, enabling immunologists and clinicians to identify the most promising epitopes for characterization in the laboratory. Attempts are also ongoing in systems biology to delineate the signaling networks in immune cells. Here we present all possible in silico models developed thus far in these areas.

Identification of conformational B-cell Epitopes in an antigen from its primary sequence.

BACKGROUND: One of the major challenges in the field of vaccine design is to predict conformational B-cell epitopes in an antigen. In the past, several methods have been developed for predicting conformational B-cell epitopes in an antigen from its tertiary structure. This is the first attempt in this area to predict conformational B-cell epitope in an antigen from its amino acid sequence. RESULTS: All Support vector machine (SVM) models were trained and tested on 187 non-redundant protein chains consisting of 2261 antibody interacting residues of B-cell epitopes. Models have been developed using binary profile of pattern (BPP) and physiochemical profile of patterns (PPP) and achieved a maximum MCC of 0.22 and 0.17 respectively. In this study, for the first time SVM model has been developed using composition profile of patterns (CPP) and achieved a maximum MCC of 0.73 with accuracy 86.59%. We compare our CPP based model with existing structure based methods and observed that our sequence based model is as good as structure based methods. CONCLUSION: This study demonstrates that prediction of conformational B-cell epitope in an antigen is possible from is primary sequence. This study will be very useful in predicting conformational B-cell epitopes in antigens whose tertiary structures are not available. A web server CBTOPE has been developed for predicting B-cell epitope http://www.imtech.res.in/raghava/cbtope/.

AntigenDB: an immunoinformatics database of pathogen antigens.

The continuing threat of infectious disease and future pandemics, coupled to the continuous increase of drug-resistant pathogens, makes the discovery of new and better vaccines imperative. For effective vaccine development, antigen discovery and validation is a prerequisite. The compilation of information concerning pathogens, virulence factors and antigenic epitopes has resulted in many useful databases. However, most such immunological databases focus almost exclusively on antigens where epitopes are known and ignore those for which epitope information was unavailable. We have compiled more than 500 antigens into the AntigenDB database, making use of the literature and other immunological resources. These antigens come from 44 important pathogenic species. In AntigenDB, a database entry contains information regarding the sequence, structure, origin, etc. of an antigen with additional information such as B and T-cell epitopes, MHC binding, function, gene-expression and post translational modifications, where available. AntigenDB also provides links to major internal and external databases. We shall update AntigenDB on a rolling basis, regularly adding antigens from other organisms and extra data analysis tools. AntigenDB is available freely at http://www.imtech.res.in/raghava/antigendb and its mirror site http://www.bic.uams.edu/raghava/antigendb.

Prediction of guide strand of microRNAs from its sequence and secondary structure.

BACKGROUND: MicroRNAs (miRNAs) are produced by the sequential processing of a long hairpin RNA transcript by Drosha and Dicer, an RNase III enzymes, and form transitory small RNA duplexes. One strand of the duplex, which incorporates into RNA-induced silencing complex (RISC) and silences the gene expression is called guide strand, or miRNA; while the other strand of duplex is degraded and called the passenger strand, or miRNA*. Predicting the guide strand of miRNA is important for better understanding the RNA interference pathways. RESULTS: This paper describes support vector machine (SVM) models developed for predicting the guide strands of miRNAs. All models were trained and tested on a dataset consisting of 329 miRNA and 329 miRNA* pairs using five fold cross validation technique. Firstly, models were developed using mono-, di-, and tri-nucleotide composition of miRNA strands and achieved the highest accuracies of 0.588, 0.638 and 0.596 respectively. Secondly, models were developed using split nucleotide composition and achieved maximum accuracies of 0.553, 0.641 and 0.602 for mono-, di-, and tri-nucleotide respectively. Thirdly, models were developed using binary pattern and achieved the highest accuracy of 0.708. Furthermore, when integrating the secondary structure features with binary pattern, an accuracy of 0.719 was seen. Finally, hybrid models were developed by combining various features and achieved maximum accuracy of 0.799 with sensitivity 0.781 and specificity 0.818. Moreover, the performance of this model was tested on an independent dataset that achieved an accuracy of 0.80. In addition, we also compared the performance of our method with various siRNA-designing methods on miRNA and siRNA datasets. CONCLUSION: In this study, first time a method has been developed to predict guide miRNA strands, of miRNA duplex. This study demonstrates that guide and passenger strand of miRNA precursors can be distinguished using their nucleotide sequence and secondary structure. This method will be useful in understanding microRNA processing and can be implemented in RNA silencing technology to improve the biological and clinical research. A web server has been developed based on SVM models described in this study (http://crdd.osdd.net:8081/RISCbinder/).