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This research was conducted to assess the scope and impact of the conversion of croplands into agroforestry orchards considering characterizing agroforestry growers, identify challenges they face during the transition process, and gather their recommended strategies to address these challenges.The investigation took place in Sadar and Biral upazila within Dinajpur district of northern part of Bangladesh. To establish a sample group, an updated roster of total 266 agroforestry growers was procured from the respective Upazila Agriculture Offices. Using a simple random sampling method, 80 growers were selected for participation in this study. Data was gathered through structured and pretested interview schedules administered to respondents between April 15th and May 15th, 2022. The study evaluated ten key attributes of agroforestry growers, encompassing factors like age, educational background, farm size, farming experience, family income, extension network engagement, agricultural knowledge, innovation inclination, marketing orientation, and technological attitude. The central focus of the study centered on the transition from croplands to agroforestry orchards. The study revealed that over the period spanning 1990 to 2022, agroforestry farmers in the study region converted cropland into orchards with varying extents, ranging from 13 % to 19.83 %. The substitution of predominant crops such as rice, maize, and wheat, along with minor crops like potatoes, vegetables, mustard, garlic, turmeric, and napier grass, marked the transition to agroforestry practices. Evaluating the consequences of this transition, approximately 60.00 % of growers perceived medium-level impacts on economic aspects, while 70.00 %, 61.25 %, and 58.75 % alleged the effects on environmental conditions, household food security, and social status, respectively as also moderately. Among the challenges faced during this shift, the most prominent was the difficulty in obtaining fair prices due to intermediary involvement. Conversely, elevated production costs were regarded as a lesser concern. Respondents put forth a variety of solutions, with the primary recommendation being the organization of enhanced training programs for farmers on scientific agroforestry systems management. In contrast, the concept of fostering appropriate coordination among agroforestry practitioners garnered less attention as a potential solution. Therefore, for farmer's enhanced benefit as well as for better environmental outputs and food security, agroforestry product marketing strategy should be strengthening using site specific policy and program.

The purpose of the present study is to determine the characteristics of time evolution of various cosmological quantities, based on four models constructed for a universe undergoing accelerated expansion. This formulation is done in the framework of Kaluza-Klein space-time, for zero spatial curvature. To solve the field equations, an ansatz is chosen for each model in such a way that it leads to a signature flip of the deceleration parameter, to ensure its consistency with recent astrophysical observations indicating a change from a decelerated expansion to an accelerated expansion of the universe. Based on these four models, time evolutions of several cosmological parameters are obtained and their variations are shown graphically against time. The arbitrary constants, associated with each model, are so tuned that the model correctly predicts the values of the Hubble parameter, deceleration parameter, energy density and gravitational constant at the present time. The findings from these models are consistent with each other, and they are in agreement with the observed features. The gravitational constant (G) shows a rapid fall in the early universe, followed by an extremely slow rise which continues at the present time. Taking (G) as a constant in two of the four models, the cosmological constant is found to be independent of time. A significant finding is that the signature flip of the deceleration parameter almost coincides with the signature flip of the cosmological constant (Λ), pointing towards a relation between the accelerated expansion and the dark energy which is represented by Λ. Other plots with respect to Λ also depict dark energy’s role in governing cosmic evolution. Considering its dynamical nature, Λ is referred to as cosmological term (instead of cosmological constant) in the text. Contrary to the common trend of using arbitrary units, the SI units for all measurable quantities are used.

Fog causes enhancement of bacterial loading in the atmosphere. Current study represents the impact of occurrences of fog on the alteration of diversity of airborne bacteria and their network computed from metagenomic data of airborne samples collected at Arthauli (25.95°N, 85.10°E) situated at central Indo-Gangetic Plain (IGP) during 1–14 January 2021. A distinct bacterial diversity with a complex network is identified in foggy condition due to the enrichment of unique types of bacteria. Present investigation highlights a statistically significant enrichment of airborne pathogenic bacteria found in a unique ecosystem within air evolved due to the occurrences of fog over central IGP. In the foggy network, Cutibacterium, an opportunistic pathogen, is identified to be interacting maximum (21 edges) with other bacteria with statistically significant copresence relation, which are responsible for various infections for human beings. A 40–60% increase (p

Abstract There is an urgent need to validate new drug targets and identify small molecules that possess activity against both drug-resistant and drug-sensitive bacteria. The enzymes belonging to amino acid biosynthesis have been shown to be essential for growth in vitro, in vivo and have not been exploited much for the development of anti-tubercular agents. Here, we have identified small molecule inhibitors targeting homoserine acetyl transferase (HSAT, MetX, Rv3341) from M. tuberculosis. MetX catalyses the first committed step in L-methionine and S-adenosyl methionine biosynthesis resulting in the formation of O-acetyl-homoserine. Using CRISPRi approach, we demonstrate that conditional repression of metX resulted in inhibition of M. tuberculosis growth in vitro. We have determined steady state kinetic parameters for the acetylation of L-homoserine by Rv3341. We show that the recombinant enzyme followed Michaelis–Menten kinetics and utilizes both acetyl-CoA and propionyl-CoA as acyl-donors. High-throughput screening of a 2443 compound library resulted in identification of small molecule inhibitors against MetX enzyme from M. tuberculosis. The identified lead compounds inhibited Rv3341 enzymatic activity in a dose dependent manner and were also active against HSAT homolog from S. aureus. Molecular docking of the identified primary hits predicted residues that are essential for their binding in HSAT homologs from M. tuberculosis and S. aureus. ThermoFluor assay demonstrated direct binding of the identified primary hits with HSAT proteins. Few of the identified small molecules were able to inhibit growth of M. tuberculosis and S. aureus in liquid cultures. Taken together, our findings validated HSAT as an attractive target for development of new broad-spectrum anti-bacterial agents that should be effective against drug-resistant bacteria.

Hsp90 constitutes one of the major chaperone machinery in the cell. The Hsp70 assists Hsp90 in its client maturation though the underlying basis of the Hsp70 role remains to be explored. In the present study, using S. cerevisiae strain expressing Ssa1 as sole Ssa Hsp70, we identified novel mutations in the nucleotide-binding domain of yeast Ssa1 Hsp70 (Ssa1-T175N and Ssa1-D158N) that adversely affect the maturation of Hsp90 clients v-Src and Ste11. The identified Ssa1 amino acids critical for Hsp90 function were also found to be conserved across species such as in E.coli DnaK and the constitutive Hsp70 isoform (HspA8) in humans. These mutations are distal to the C-terminus of Hsp70, that primarily mediates Hsp90 interaction through the bridge protein Sti1, and proximal to Ydj1 (Hsp40 co-chaperone of Hsp70 family) binding region. Intriguingly, we found that the bridge protein Sti1 is critical for cellular viability in cells expressing Ssa1-T175N (A1-T175N) or Ssa1-D158N (A1-D158N) as sole Ssa Hsp70. The growth defect was specific for sti1Δ, as deletion of none of the other Hsp90 co-chaperones showed lethality in A1-T175N or A1-D158N. Mass-spectrometry based whole proteome analysis of A1-T175N cells lacking Sti1 showed an altered abundance of various kinases and transcription factors suggesting compromised Hsp90 activity. Further proteomic analysis showed that pathways involved in signaling, signal transduction, and protein phosphorylation are markedly downregulated in the A1-T175N upon repressing Sti1 expression using doxycycline regulatable promoter. In contrast to Ssa1, the homologous mutations in Ssa4 (Ssa4-T175N/D158N), the stress inducible Hsp70 isoform, supported cell growth even in the absence of Sti1. Overall, our data suggest that Ydj1 competes with Hsp90 for binding to Hsp70, and thus regulates Hsp90 interaction with the nucleotide-binding domain of Hsp70. The study thus provides new insight into the Hsp70-mediated regulation of Hsp90 and broadens our understanding of the intricate complexities of the Hsp70-Hsp90 network.

Abstract Background c-Myc plays an important role in cell proliferation, cell growth and in differentiation, making it a key regulator for carcinogenesis and pluripotency. Tight control of c-myc turnover is required by ubiquitin-mediated degradation. This is achieved in the system by two F-box proteins Skp2 and FBXW7. Results Dynamic modelling technique was used to build two exclusive models for phosphorylation dependent degradation of Myc by FBXW7 (Model 1) and phosphorylation independent degradation by Skp2 (Model 2). Sensitivity analysis performed on these two models revealed that these models were corroborating experimental studies. It was also seen that Model 1 was more robust and perhaps more efficient in degrading c-Myc. These results questioned the existence of the two models in the system and to answer the question a combined model was hypothesised which had a decision making switch. The combined model had both Skp2 and FBXW7 mediated degradation where again the latter played a more important role. This model was able to achieve the lowest levels of ubiquitylated Myc and therefore functioned most efficiently in degradation of Myc. Conclusion In this report, c-Myc degradation by two F-box proteins was mathematically evaluated based on the importance of c-Myc turnover. The study was performed in a homeostatic system and therefore, prompts the exploration of c-Myc degradation in cancer state and in pluripotent state.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of coronavirus induced disease-2019 (COVID-19), is a type of common cold virus responsible for a global pandemic which requires immediate measures for its containment. India has the world’s largest population aged between 10 and 40 years. At the same time, India has a large number of individuals with diabetes, hypertension and kidney diseases, who are at a high risk of developing COVID-19. A vaccine against the SARS-CoV-2, may offer immediate protection from the causative agent of COVID-19, however, the protective memory may be short-lived. Even if vaccination is broadly successful in the world, India has a large and diverse population with over one-third being below the poverty line. Therefore, the success of a vaccine, even when one becomes available, is uncertain, making it necessary to focus on alternate approaches of tackling the disease. In this review, we discuss the differences in COVID-19 death/infection ratio between urban and rural India; and the probable role of the immune system, co-morbidities and associated nutritional status in dictating the death rate of COVID-19 patients in rural and urban India. Also, we focus on strategies for developing masks, vaccines, diagnostics and the role of drugs targeting host-virus protein-protein interactions in enhancing host immunity. We also discuss India’s strengths including the resources of medicinal plants, good food habits and the role of information technology in combating COVID-19. We focus on the Government of India’s measures and strategies for creating awareness in the containment of COVID-19 infection across the country.

Two-component systems (TCSs) are central to the ability of Mycobacterium tuberculosis to respond to stress. One such paired TCS is SenX3-RegX3, which responds to phosphate starvation. Here we show that RegX3 is required for M. tuberculosis to withstand low pH, one of the challenges encountered by the bacterium in the host environment, and that RegX3 activates the cytosolic redox sensor WhiB3 to launch an appropriate response to acid stress. We show that the whiB3 promoter of M. tuberculosis harbors a RegX3 binding motif. Electrophoretic mobility shift assays (EMSAs) show that phosphorylated RegX3 (RegX3-P) (but not its unphosphorylated counterpart) binds to this motif, whereas a DNA binding mutant, RegX3 (K204A) fails to do so. Mutation of the putative RegX3 binding motif on the whiB3 promoter, abrogates the binding of RegX3-P. The significance of this binding is established by demonstrating that the expression of whiB3 is significantly attenuated under phosphate starvation or under acid stress in the regX3-inactivated mutant, ΔregX3. Green fluorescent protein (GFP)-based reporter assays further confirm the requirement of RegX3 for the activation of the whiB3 promoter. The compromised survival of ΔregX3 under acid stress and its increased trafficking to the lysosomal compartment are reversed upon complementation with either regX3 or whiB3, suggesting that RegX3 exerts its effects in a WhiB3-dependent manner. Finally, using an in vitro granuloma model, we show that granuloma formation is compromised in the absence of regX3, but restored upon complementation with either regX3 or whiB3. Our findings provide insight into an important role of RegX3 in the network that regulates the survival of M. tuberculosis under acid stress similar to that encountered in its intracellular niche. Our results argue strongly in favor of a role of the RegX3-WhiB3 axis in establishment of M. tuberculosis infection.

Photovoltaics has become a significant branch of next-generation sustainable energy production. Kesterite Cu2ZnSn(S, Se)4 (copper-zinc-tin-(sulfur, selenium) or CZTS(Se)) is considered one of the most promising, earth-abundant, and nontoxic candidates for solar energy generation over the last decade. However, shallow phase stability of the quaternary phase and the presence of various secondary phases and defects are the main hindrances in achieving the target device performance. This paper summarizes various approaches to synthesize the CZTS absorber layer and the CdS n-type material layer. Besides, different CZTS solar cell device structures, as well as a comprehensive review of secondary phases and defects, have been illustrated and discussed. At last, this review is intended to highlight the current challenges and prospects of CZTS solar cells.

Abstract Successful implantation is dependent on the appropriate decidualization of endometrial stromal cells for the establishment of pregnancy in women. Mycobacterial heat shock protein 65 (HSP65) is involved in pathogenesis of the genital tuberculosis (GTB), one of the common causes of infertility in emerging countries. Though implantation failure appears to be the major cause, understanding the status of decidualizaiton process in women diagnosed with GTB has not been thoroughly addressed. We, therefore, explored the effect of HSP65 protein on the endometrial cell metabolism during in vitro decidualization. In order to identify the cellular metabolism of decidual cells with and without HSP65 treatment, proton NMR based characterization of metabolites extracted from cells and culture media were performed. In presence of HSP65, significant reduction in the decidual phenotype of endometrial stromal cells and prolactin expression is suggestive of impairment in decidualization. The intracellular and extracellular metabolic changes in HSP65 treated endometrial stromal cells produced a distinct pattern, reflecting the interaction between the protein and cellular metabolism. HSP65 mediated dysregulation in cellular metabolism is associated with poor decidualization. Besides enriching the present knowledge on metabolic changes underlying stromal cells decidualization, these findings assist in identifying potential molecular causes for decidualization failure in GTB women.

Abstract Background Myc is an essential gene having multiple functions such as in cell growth, differentiation, apoptosis, genomic stability, angiogenesis, and disease biology. A large number of researchers dedicated to Myc biology are generating a substantial amount of data in normal and cancer cells/tissues including Burkitt’s lymphoma and ovarian cancer. Results MYCbase ( http://bicresources.jcbose.ac.in/ssaha4/mycbase ) is a collection of experimentally supported functional sites in Myc that can influence the biological cellular processes. The functional sites were compiled according to their role which includes mutation, methylation pattern, post-translational modifications, protein-protein interactions (PPIs), and DNA interactions. In addition, biochemical properties of Myc are also compiled, which includes metabolism/pathway, protein abundance, and modulators of protein-protein interactions. The OMICS data related to Myc- like gene expression, proteomics expression using mass-spectrometry and miRNAs targeting Myc were also compiled in MYCbase. The mutation and pathway data from the MYCbase were analyzed to look at the patterns and distributions across different diseases. There were few proteins/genes found common in Myc-protein interactions and Myc-DNA binding, and these can play a significant role in transcriptional feedback loops. Conclusion In this report, we present a comprehensive integration of relevant information regarding Myc in the form of MYCbase. The data compiled in MYCbase provides a reliable data resource for functional sites at the residue level and biochemical properties of Myc in various cancers.

ABSTRACT Comprehensive knowledge of host-pathogen interactions is central to understand the life cycle of a pathogen and devise specific therapeutic strategies. Protein-protein interactions (PPIs) are key mediators of host-pathogen interactions. Hepatitis E virus (HEV) is a major cause of viral hepatitis in humans. Recent reports also demonstrate its extrahepatic manifestations in the brain. Toward understanding the molecular details of HEV life cycle, we screened human liver and fetal brain cDNA libraries to identify the host interaction partners of proteins encoded by genotype 1 HEV and constructed the virus-host PPI network. Analysis of the network indicated a role of HEV proteins in modulating multiple host biological processes such as stress and immune responses, the ubiquitin-proteasome system, energy and iron metabolism, and protein translation. Further investigations revealed the presence of multiple host translation regulatory factors in the viral translation/replication complex. Depletion of host translation factors such as eIF4A2, eIF3A, and RACK1 significantly reduced the viral replication, whereas eIF2AK4 depletion had no effect. These findings highlight the ingenuity of the pathogen in manipulating the host machinery to its own benefit, a clear understanding of which is essential for the identification of strategic targets and development of specific antivirals against HEV. IMPORTANCE Hepatitis E virus (HEV) is a pathogen that is transmitted by the fecal-oral route. Owing to the lack of an efficient laboratory model, the life cycle of the virus is poorly understood. During the course of infection, interactions between the viral and host proteins play essential roles, a clear understanding of which is essential to decode the life cycle of the virus. In this study, we identified the direct host interaction partners of all HEV proteins and generated a PPI network. Our functional analysis of the HEV-human PPI network reveals a role of HEV proteins in modulating multiple host biological processes such as stress and immune responses, the ubiquitin-proteasome system, energy and iron metabolism, and protein translation. Further investigations revealed an essential role of several host factors in HEV replication. Collectively, the results from our study provide a vast resource of PPI data from HEV and its human host and identify the molecular components of the viral translation/replication machinery.

Protein-peptide interactions form an important subset of the total protein interaction network in the cell and play key roles in signaling and regulatory networks, and in major biological processes like cellular localization, protein degradation, and immune response. In this work, we have described the LMDIPred web server, an online resource for generalized prediction of linear peptide sequences that may bind to three most prevalent and well-studied peptide recognition modules (PRMs)-SH3, WW and PDZ. We have developed support vector machine (SVM)-based prediction models that achieved maximum Matthews Correlation Coefficient (MCC) of 0.85 with an accuracy of 94.55% for SH3, MCC of 0.90 with an accuracy of 95.82% for WW, and MCC of 0.83 with an accuracy of 92.29% for PDZ binding peptides. LMDIPred output combines predictions from these SVM models with predictions using Position-Specific Scoring Matrices (PSSMs) and string-matching methods using known domain-binding motif instances and regular expressions. All of these methods were evaluated using a five-fold cross-validation technique on both balanced and unbalanced datasets, and also validated on independent datasets. LMDIPred aims to provide a preliminary bioinformatics platform for sequence-based prediction of probable binding sites for SH3, WW or PDZ domains.

Gut microbiota of higher vertebrates is host-specific. The number and diversity of the organisms residing within the gut ecosystem are defined by physiological and environmental factors, such as host genotype, habitat, and diet. Recently, culture-independent sequencing techniques have added a new dimension to the study of gut microbiota and the challenge to analyze the large volume of sequencing data is increasingly addressed by the development of novel computational tools and methods. Interestingly, gut microbiota maintains a constant relative abundance at operational taxonomic unit (OTU) levels and altered bacterial abundance has been associated with complex diseases such as symptomatic atherosclerosis, type 2 diabetes, obesity, and colorectal cancer. Therefore, the study of gut microbial population has emerged as an important field of research in order to ultimately achieve better health. In addition, there is a spontaneous, non-linear, and dynamic interaction among different bacterial species residing in the gut. Thus, predicting the influence of perturbed microbe–microbe interaction network on health can aid in developing novel therapeutics. Here, we summarize the population abundance of gut microbiota and its variation in different clinical states, computational tools available to analyze the pyrosequencing data, and gut microbe–microbe interaction networks.

Post-translational modification pathways such as SUMOylation are integral to all cellular processes and tissue homeostasis. We investigated the possible involvement of SUMOylation in the epithelial signalling in Crohn's disease (CD) and ulcerative colitis (UC), the two major forms of inflammatory bowel disease (IBD). Initially in a murine model of IBD, induced by dextran–sulfate–sodium (DSS mice), we observed inflammation accompanied by a lowering of global SUMOylation of colonic epithelium. The observed SUMOylation alteration was due to a decrease in the sole SUMO E2 enzyme (Ubc9). Mass-spectrometric analysis revealed the existence of a distinct SUMOylome (SUMO-conjugated proteome) in DSS mice with alteration of key cellular regulators, including master kinase Akt1. Knocking-down of Ubc9 in epithelial cells resulted in dramatic activation of inflammatory gene expression, a phenomenon that acted via reduction in Akt1 and its SUMOylated form. Importantly, a strong decrease in Ubc9 and Akt1 was also seen in endoscopic biopsy samples (N = 66) of human CD and UC patients. Furthermore, patients with maximum disease indices were always accompanied by severely lowered Ubc9 or SUMOylated-Akt1. Mucosal tissues with severely compromised Ubc9 function displayed higher levels of pro-inflammatory cytokines and compromised wound-healing markers. Thus, our results reveal an important and previously undescribed role for the SUMOylation pathway involving Ubc9 and Akt1 in modulation of epithelial inflammatory signalling in IBD.

PPIMpred is a web server that allows high-throughput screening of small molecules for targeting specific protein–protein interactions, namely Mdm2/P53, Bcl2/Bak and c-Myc/Max. Three different kernels of support vector machine (SVM), namely, linear, polynomial and radial basis function (RBF), and two other machine learning techniques including Naive Bayes and Random Forest were used to train the models. A fivefold cross-validation technique was used to measure the performance of these classifiers. The RBF kernel of SVM outperformed and/or was comparable with all other methods with accuracy values of 83%, 79% and 90% for Mdm2/P53, Bcl2/Bak and c-Myc/Max, respectively. About 80% of the predicted SVM scores of training/testing datasets from Mdm2/P53 and Bcl2/Bak have significant IC50 values and docking scores. The proposed models achieved an accuracy of 66–90% with blind sets. The three mentioned (Mdm2/P53, Bcl2/Bak and c-Myc/Max) proposed models were screened in a large dataset of 265 242 small chemicals from National Cancer Institute open database. To further realize the robustness of this approach, hits with high and random SVM scores were used for molecular docking in AutoDock Vina wherein the molecules with high and random predicted SVM scores yielded moderately significant docking scores (p-values

A considerable proportion of protein-protein interactions (PPIs) in the cell are estimated to be mediated by very short peptide segments that approximately conform to specific sequence patterns known as linear motifs (LMs), often present in the disordered regions in the eukaryotic proteins. These peptides have been found to interact with low affinity and are able bind to multiple interactors, thus playing an important role in the PPI networks involving date hubs. In this work, PPI data and de novo motif identification based method (MEME) were used to identify such peptides in three cancer-associated hub proteins-MYC, APC and MDM2. The peptides corresponding to the significant LMs identified for each hub protein were aligned, the overlapping regions across these peptides being termed as overlapping linear peptides (OLPs). These OLPs were thus predicted to be responsible for multiple PPIs of the corresponding hub proteins and a scoring system was developed to rank them. We predicted six OLPs in MYC and five OLPs in MDM2 that scored higher than OLP predictions from randomly generated protein sets. Two OLP sequences from the C-terminal of MYC were predicted to bind with FBXW7, component of an E3 ubiquitin-protein ligase complex involved in proteasomal degradation of MYC. Similarly, we identified peptides in the C-terminal of MDM2 interacting with FKBP3, which has a specific role in auto-ubiquitinylation of MDM2. The peptide sequences predicted in MYC and MDM2 look promising for designing orthosteric inhibitors against possible disease-associated PPIs. Since these OLPs can interact with other proteins as well, these inhibitors should be specific to the targeted interactor to prevent undesired side-effects. This computational framework has been designed to predict and rank the peptide regions that may mediate multiple PPIs and can be applied to other disease-associated date hub proteins for prediction of novel therapeutic targets of small molecule PPI modulators.

Protein-protein interactions in Escherichia coli (E. coli) has been studied extensively using high throughput methods such as tandem affinity purification followed by mass spectrometry and yeast two-hybrid method. This can in turn be used to understand the mechanisms of bacterial cellular processes. However, experimental characterization of such huge amount of interactions data is not available for other important enteropathogens. Here, we propose a support vector machine (SVM)-based prediction model using the known PPIs data of E. coli that can be used to predict PPIs in other enteropathogens, such as Vibrio cholerae, Salmonella Typhi, Shigella flexneri and Yersinia entrocolitica. Different features such as domain-domain association (DDA), network topology, and sequence information were used in developing the SVM model. The proposed model using DDA, degree and amino acid composition features has achieved an accuracy of 82% and 62% on 5-fold cross validation and blind E. coli datasets, respectively. The predicted interactions were validated by Gene Ontology (GO) semantic similarity measure and String PPIs database (experimental PPIs only). Finally, we have developed a user-friendly webserver named EnPPIpred to predict intra-species PPIs in enteropathogens, which will be of great help for the experimental biologists. The webserver EnPPIpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/EnPPIpred/.

Fungal allergy is considered as serious health problem worldwide and is increasing at an alarming rate in the industrialized areas. Rhizopus oyzae is a ubiquitously present airborne pathogenic mold and an important source of inhalant allergens for the atopic population of India. Here, we report the biochemical and immunological features of its 44 kDa sero-reactive aspartic protease allergen, which is given the official designation 'Rhi o 1'.The natural Rhi o 1 was purified by sequential column chromatography and its amino acid sequence was determined by mass spectrometry and N-terminal sequencing. Based on its amino acid sequence, the cDNA sequence was identified, cloned and expressed to produce recombinant Rhi o 1. The allergenic activity of rRhi o 1 was assessed by means of its IgE reactivity and histamine release ability. The biochemical property of Rhi o 1 was studied by enzyme assay. IgE-inhibition experiments were performed to identify its cross-reactivity with the German cockroach aspartic protease allergen Bla g 2. For precise characterization of the cross-reactive epitope, we used anti-Bla g 2 monoclonal antibodies for their antigenic specificity towards Rhi o 1. A homology based model of Rhi o 1 was built and mapping of the cross-reactive conformational epitope was done using certain in silico structural studies.The purified natural nRhi o 1 was identified as an endopeptidase. The full length allergen cDNA was expressed and purified as recombinant rRhi o 1. Purified rRhi o 1 displayed complete allergenicity similar to the native nRhi o 1. It was recognized by the serum IgE of the selected mold allergy patients and efficiently induced histamine release from the sensitized PBMC cells. This allergen was identified as an active aspartic protease functional in low pH. The Rhi o 1 showed cross reactivity with the cockroach allergen Bla g 2, as it can inhibit IgE binding to rBla g 2 up to certain level. The rBla g 2 was also found to cross-stimulate histamine release from the effector cells sensitized with anti-Rhi o 1 serum IgE. This cross-reactivity was found to be mediated by a common mAb4C3 recognizable conformational epitope. Bioinformatic studies revealed high degree of structural resemblances between the 4C3 binding sites of both the allergens.The present study reports for the first time anew fungal aspartic protease allergen designated as Rhi o 1, which triggers IgE-mediated sensitization leading to various allergic diseases. Here we have characterized the recombinant Rhi o 1 and its immunological features including cross-reactive epitope information that will facilitate the component-resolved diagnosis of mold allergy.

Long noncoding RNAs (lncRNAs) are noncoding transcripts longer than 200 nucleotides, which show evidence of pervasive transcription and participate in a plethora of cellular regulatory processes. Although several noncoding transcripts have been functionally annotated as lncRNAs within the genome, not all have been proven to fulfill the criteria for a functional regulator and further analyses have to be done in order to include them in a functional cohort. LncRNAs are being classified and reclassified in an ongoing annotation process, and the challenge is fraught with ambiguity, as newer evidences of their biogenesis and functional implication come into light. In our effort to understand the complexity of this still enigmatic biomolecule, we have developed a new database entitled "LncRBase" where we have classified and characterized lncRNAs in human and mouse. It is an extensive resource of human and mouse lncRNA transcripts belonging to fourteen distinct subtypes, with a total of 83,201 entries for mouse and 133,361 entries for human: among these, we have newly annotated 8,507 mouse and 14,813 human non coding RNA transcripts (from UCSC and H-InvDB 8.0) as lncRNAs. We have especially considered protein coding gene loci which act as hosts for non coding transcripts. LncRBase includes different lncRNA transcript variants of protein coding genes within LncRBase. LncRBase provides information about the genomic context of different lncRNA subtypes, their interaction with small non coding RNAs (ncRNAs) viz. piwi interacting RNAs (piRNAs) and microRNAs (miRNAs) and their mode of regulation, via association with diverse other genomic elements. Adequate knowledge about genomic origin and molecular features of lncRNAs is essential to understand their functional and behavioral complexities. Overall, LncRBase provides a thorough study on various aspects of lncRNA origin and function and a user-friendly interface to search for lncRNA information. LncRBase is available at http://bicresources.jcbose.ac.in/zhumur/lncrbase.

BACKGROUND: Salmonella Typhi is a human-restricted pathogen, which causes typhoid fever and remains a global health problem in the developing countries. Although previously reported host expression datasets had identified putative biomarkers and therapeutic targets of typhoid fever, the underlying molecular mechanism of pathogenesis remains incompletely understood. METHODS: We used five gene expression datasets of human peripheral blood from patients suffering from S. Typhi or other bacteremic infections or non-infectious disease like leukemia. The expression datasets were merged into human protein interaction network (PIN) and the expression correlation between the hubs and their interacting proteins was measured by calculating Pearson Correlation Coefficient (PCC) values. The differences in the average PCC for each hub between the disease states and their respective controls were calculated for studied datasets. The individual hubs and their interactors with expression, PCC and average PCC values were treated as dynamic subnetworks. The hubs that showed unique trends of alterations specific to S. Typhi infection were identified. RESULTS: We identified S. Typhi infection-specific dynamic subnetworks of the host, which involve 81 hubs and 1343 interactions. The major enriched GO biological process terms in the identified subnetworks were regulation of apoptosis and biological adhesions, while the enriched pathways include cytokine signalling in the immune system and downstream TCR signalling. The dynamic nature of the hubs CCR1, IRS2 and PRKCA with their interactors was studied in detail. The difference in the dynamics of the subnetworks specific to S. Typhi infection suggests a potential molecular model of typhoid fever. CONCLUSIONS: Hubs and their interactors of the S. Typhi infection-specific dynamic subnetworks carrying distinct PCC values compared with the non-typhoid and other disease conditions reveal new insight into the pathogenesis of S. Typhi.

BACKGROUND: Viral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs) has great implication for therapeutics. METHODS: In this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques. RESULTS: Out of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49%) and Random Forest (55.66%). However the specificity of Naïve Bayes was the highest (99.52%) as compared with SVM (74%) and Random Forest (89.08%). Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus "C protein" binds to membrane docking protein, while "X protein" and "P protein" interacts with cell-killing and metabolic process proteins, respectively. CONCLUSION: The proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV), interacting partners of host protein were identified using optimised SVM model.