Evaluation of potential role of R-loop and G-quadruplex DNA in the fragility of c-MYC during chromosomal translocation associated with Burkitt's lymphoma.

t(8;14) translocation is the hallmark of Burkitt's lymphoma and results in c-MYC deregulation. During the translocation, c-MYC gene on chromosome 8 gets juxtaposed to the Ig switch regions on chromosome 14. Although the promoter of c-MYC has been investigated for its mechanism of fragility, little is known about other c-MYC breakpoint regions. We have analyzed the translocation break points at the exon 1/intron 1 of c-MYC locus from patients with Burkitt's lymphoma. Results showed that the breakpoint region, when present on a plasmid, could fold into an R-loop confirmation in a transcription-dependent manner. Sodium bisulfite modification assay revealed significant single-strandedness on chromosomal DNA of Burkitt's lymphoma cell line, Raji, and normal lymphocytes, revealing distinct R-loops covering up to 100 bp region. Besides, ChIP-DRIP analysis reveals that the R-loop antibody can bind to the breakpoint region. Further, we show the formation of stable parallel intramolecular G-quadruplex on non-template strand of the genome. Finally, incubation of purified AID in vitro or overexpression of AID within the cells led to enhanced mutation frequency at the c-MYC breakpoint region. Interestingly, anti-γH2AX can bind to DSBs generated at the c-MYC breakpoint region within the cells. The formation of R-loop and G-quadruplex was found to be mutually exclusive. Therefore, our results suggest that AID can bind to the single-stranded region of the R-loop and G4 DNA, leading to the deamination of cytosines to uracil and induction of DNA breaks in one of the DNA strands, leading to double-strand break, which could culminate in t(8;14) chromosomal translocation.

Disease suppression, growth promotion and colonization attributes of resident endophytic bacteria against white root rot (Dematophora necatrix Hartig) of apple.

The inherent potential of apple plants was investigated to explore bacterial endophytes and their role in suppressing Dematophora necatrix, the causative pathogen of white root rot disease. Resultantly 34 endophytic bacteria isolated from healthy apple plants, and subsequently 6 most efficient isolates viz., Bacillus megaterium strain EA3, Enterobacter sp. strain EA7, Bacillus megaterium strain EK2, Stenotrophomonas maltophilia strain EK6, Acinetobacter nosocomialis strain ES2 and Pseudomonas aeruginosa strain ES8 depicting anti-pathogen interactions through preliminary screening were assessed further under in vitro, glasshouse and field conditions against white root rot pathogen/disease. Maximum mycelial growth inhibition (80.37%) was obtained with S. maltophilia strain EK6 encouraging for its seed treatment and soil application thereby providing significant effective control (87.91%) of white root rot under glasshouse conditions to other five bacterial endophytes evaluated simultaneously, followed by field efficacy of 83.70%. In addition, it has significantly enhanced the growth parameters of apple trees under both glasshouse and field conditions. The inoculated healthy plants were assessed for endophytic colonization which revealed maximum endosphere colonialism by S. maltophilia strain EK6. Additionally, confocal microscopic images of transverse sections of root cells colonized by bacterial endophytes as compared to untreated control implied their persistence and establishment in endosphere of apple seedlings. The study provides the first report on interaction between apple associated bacterial root endophytes and D. necatrix. The obtained endophytic strains could be employed as alternative for mitigating white root rot disease in future.

Prediction of Mycobacterium tuberculosis cell wall permeability using machine learning methods.

Tuberculosis (TB) caused by the bacteria Mycobacterium tuberculosis (M. tb), continues to pose a significant worldwide health threat. The advent of drug-resistant strains of the disease highlights the critical need for novel treatments. The unique cell wall of M. tb provides an extra layer of protection for the bacteria and hence only compounds that can penetrate this barrier can reach their targets within the bacterial cell wall. The creation of a reliable machine learning (ML) model to predict the mycobacterial cell wall permeability of small molecules is presented in this work and four ML algorithms, including Random Forest, Support Vector Machines (SVM), k-nearest Neighbour (k-NN) and Logistic Regression were trained on a dataset of 5368 compounds. RDKit and Mordred toolkits were used to calculate features. To determine the most effective model, various performance metrics were used such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve. The best-performing model was further refined with hyperparameter tuning and tenfold cross-validation. The SVM model with filtering outperformed the other machine learning models and demonstrated 80.26% and 81.13% accuracy on the test and validation datasets, respectively. The study also provided insights into the molecular descriptors that play the most important role in predicting the ability of a molecule to pass the M. tb cell wall, which could guide future compound design. The model is available at https://github.com/PGlab-NIPER/MTB_Permeability .

In vitro assessment of newer colistin-sparing antimicrobial agents for clinical isolates of carbapenem-resistant organisms.

OBJECTIVES: Carbapenem-resistant organisms (CROs) are a significant public health threat globally, particularly in countries like India with high antibiotic resistance rates. The current study investigates the prevalence of CROs, detects resistance mechanisms using phenotypic methods and assesses the efficacy of newer antibiotics against CROs. METHODS: A prospective study conducted at a tertiary care hospital in India during 2021-23. Clinical specimens were processed and bacterial identification was performed using MALDI-TOF mass spectrometry followed by antimicrobial susceptibility testing using CLSI guidelines against a plethora of newer antibiotics for CROs. Carbapenemase production was detected using phenotypic methods, and the presence of the mcr-1 gene was assessed by real-time PCR. RESULTS: During the study period, predominantly (70 %) Gram-negative bacteria were isolated; amongst which 5692 strains were carbapenem-resistant, wherein resistance to different carbapenems ranged from 34.1% to 79 %. Majority of the carbapenemase producers were metallo-ß-lactamases (MBL) producers (75 %). Colistin resistance was noted in 5.4 % of selected carbapenem-resistant isolates. Among newer antibiotics, cefiderocol demonstrated the lowest resistance rates (0-14 %), while resistance to newer ß-lactam/ß-lactamase inhibitor combinations was very high in carbapenem-resistant isolates. Fosfomycin, minocycline and tigecycline, each showing variable efficacy depending on the site of infection. Moreover, newer ß-lactam/ß-lactamase inhibitor combinations offer restricted benefits due to widespread prevalence of MBL in the region. CONCLUSION: The escalating prevalence of CROs in India underscores the urgency for alternative treatment options beyond colistin. Hence, highlighting the critical importance of developing effective strategies to combat carbapenem resistance.

OlfactionBase: a repository to explore odors, odorants, olfactory receptors and odorant-receptor interactions.

Olfaction is a multi-stage process that initiates with the odorants entering the nose and terminates with the brain recognizing the odor associated with the odorant. In a very intricate way, the process incorporates various components functioning together and in synchronization. OlfactionBase is a free, open-access web server that aims to bring together knowledge about many aspects of the olfaction mechanism in one place. OlfactionBase contains detailed information of components like odors, odorants, and odorless compounds with physicochemical and ADMET properties, olfactory receptors (ORs), odorant- and pheromone binding proteins, OR-odorant interactions in Human and Mus musculus. The dynamic, user-friendly interface of the resource facilitates exploration of different entities: finding chemical compounds having desired odor, finding odorants associated with OR, associating chemical features with odor and OR, finding sequence information of ORs and related proteins. Finally, the data in OlfactionBase on odors, odorants, olfactory receptors, human and mouse OR-odorant pairs, and other associated proteins could aid in the inference and improved understanding of odor perception, which might provide new insights into the mechanism underlying olfaction. The OlfactionBase is available at https://bioserver.iiita.ac.in/olfactionbase/.

Flavonoids for treating pulmonary fibrosis: Present status and future prospects.

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with an unknown underlying cause. There is no complete cure for IPF; however, two anti-fibrotic agents (Nintedanib and pirfenidone) are approved by the USFDA to extend the patient's life span. Therefore, alternative therapies supporting the survival of fibrotic patients have been studied in recent literature. The abundance of phenolic compounds, particularly flavonoids, has gathered attention due to their potential health benefits. Various flavonoids, like naringin, quercetin, baicalin, baicalein, puerarin, silymarin, and kaempferol, exhibit anti-inflammatory and anti-oxidant properties, which help decrease lung fibrosis. Various databases, including PubMed, EBSCO, ProQuest, and Scopus, as well as particular websites, such as the World Health Organisation and the National Institutes of Health, were used to conduct a literature search. Several mechanisms of action of flavonoids are reported with the help of in vivo and cell line studies emphasizing their ability to modulate oxidative stress, inflammation, and fibrotic processes in the lungs. They are reported for the restoration of biomarkers like hydroxyproline, cytokines, superoxide dismutase, malondialdehyde and others associated with IPF and for modulating various pathways responsible for the progression of pulmonary fibrosis. Yet, flavonoids have some drawbacks, such as poor solubility, challenging drug loading, stability issues, and scarce bioavailability. Therefore, novel formulations of flavonoids are explored, including liposomes, solid lipid microparticles, polymeric nanoparticles, nanogels, and nanocrystals, to enhance the therapeutic efficacy of flavonoids in pulmonary fibrosis. This review focuses on the role of flavonoids in mitigating idiopathic pulmonary fibrosis, their mode of action and novel formulations.

Improving Oxygen Reduction Performance of Surface-Layer-Controlled Pt-Ni Nano-Octahedra via Gaseous Etching.

This study demonstrates an atomic composition manipulation on Pt-Ni nano-octahedra to enhance their electrocatalytic performance. By selectively extracting Ni atoms from the {111} facets of the Pt-Ni nano-octahedra using gaseous carbon monoxide at an elevated temperature, a Pt-rich shell is formed, resulting in an ∼2 atomic layer Pt-skin. The surface-engineered octahedral nanocatalyst exhibits a significant enhancement in both mass activity (∼1.8-fold) and specific activity (∼2.2-fold) toward the oxygen reduction reaction compared with its unmodified counterpart. After 20,000 potential cycles of durability tests, the surface-etched Pt-Ni nano-octahedral sample shows a mass activity of 1.50 A/mgPt, exceeding the initial mass activity of the unetched counterpart (1.40 A/mgPt) and outperforming the benchmark Pt/C (0.18 A/mgPt) by a factor of 8. DFT calculations predict this improvement with the Pt surface layers and support these experimental observations. This surface-engineering protocol provides a promising strategy for developing novel electrocatalysts with improved catalytic features.

Physical adsorption and oxidation of ultra-thin MoS2crystals: insights into surface engineering for 2D electronics and beyond.

The oxidation mechanism of atomically thin molybdenum disulfide (MoS2) plays a critical role in its nanoelectronics, optoelectronics, and catalytic applications, where devices often operate in an elevated thermal environment. In this study, we systematically investigate the oxidation of mono- and few-layer MoS2flakes in the air at temperatures ranging from 23 °C to 525 °C and relative humidities of 10%-60% by using atomic force microscopy (AFM), Raman spectroscopy and x-ray photoelectron spectroscopy. Our study reveals the formation of a uniform nanometer-thick physical adsorption layer on the surface of MoS2, which is attributed to the adsorption of ambient moisture. This physical adsorption layer acts as a thermal shield of the underlying MoS2lattice to enhance its thermal stability and can be effectively removed by an AFM tip scanning in contact mode or annealing at 400 °C. Our study shows that high-temperature thermal annealing and AFM tip-based cleaning result in chemical adsorption on sulfur vacancies in MoS2, leading to p-type doping. Our study highlights the importance of humidity control in ensuring reliable and optimal performance for MoS2-based electronic and electrochemical devices and provides crucial insights into the surface engineering of MoS2, which are relevant to the study of other two-dimensional transition metal dichalcogenide materials and their applications.

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differentiation) through complex signaling pathways. The FGFR1 growth receptor is essential in the pathogenesis of numerous malignancies, including but not limited to breast cancer, bladder cancer, hepatocellular carcinoma (HCC), and cholangiocarcinoma. The higher levels of FGFR1 expression on the surface of cancer cells cause overly active signaling, which leads to rapid cell proliferation, resulting in a high spread of cancer cells. The kinases that FGFR1 activates migrate across the cell nucleus, activating genes and kinase proteins necessary for the growth and survival of cancerous cells. Therefore, FGFR1 targeting shows therapeutic promise in some diseases, including cancer. Inhibitors of FGFR1s are being developed and studied for their potential to block aberrant FGFR1 signaling and inhibit cancer growth. Since the discovery of new FGFR1 inhibitors in the laboratory is difficult, expensive, time-consuming, and labor-intensive, only a small number of FGFR1 inhibitors have been approved by the FDA for use in the treatment of cancer. To accelerate drug discovery by efficiently exploring the vast chemical space, and identifying potential candidates with higher accuracy and reduced cost, we developed artificial intelligence (AI)-based prediction models for FGFR1 inhibitors using a dataset of 2356 chemical compounds. Four machine learning (ML) algorithms (SVM, RF, k-NN, and ANN) were used to train different prediction models based on molecular descriptors (1D and 2D, with and without molecular fingerprints). Among all trained models, the random forest (RF)-based prediction model achieved the highest accuracy on the training (98.9%), test (89.8%), and external test (90.3%) datasets. The developed inhibitor prediction model (FGFR1Pred) provides a valuable tool for identifying potential FGFR1 inhibitors, expediting the drug discovery process and ultimately facilitating the development of new therapeutics. The model is made available at https://github.com/PGlab-NIPER/FGFR1Pred.git.

TNFipred: a classification model to predict TNF-α inhibitors.

Rheumatoid arthritis (RA), characterized by severe inflammation in the joint lining, is a progressive, chronic, autoimmune disorder with high morbidity and mortality rates. There are several mechanisms responsible for joint damage, but overproduction of TNF-α is a significant mechanism that results in excess swelling and pain. Drugs acting on TNF-α are known to significantly reduce the disease progression and improve the quality of life in many RA patients. Hence, inhibiting TNF-α is considered one of the most effective treatments for RA. Currently, there are only a few FDA-approved TNF-α inhibitors, which are mainly monoclonal antibodies, fusion proteins, or biosimilars with disadvantages such as poor stability, difficulty in route of administration (often given as injection or infusion), cost-prohibitive large-scale production, and increased side effects. There are just a handful of small compounds known to have TNF- inhibitory capabilities. Thus, there is a dire need for new drugs, especially small molecules in the market, such as TNF-α inhibitors. The conventional method of identifying TNF-α inhibitors is expensive, labor, and time intensive. Machine learning (ML) can be used to solve existing drug discovery and development problems. In this study, four classification algorithms-naïve Bayes (NB), random forest (RF), k-nearest neighbor (kNN), and support vector machine (SVM)-were used to train ML models for classifying TNF-α inhibitors based on three sets of features. The performance of the RF model was found to be best when using 1D, 2D, and fingerprints as features, with an accuracy of 87.96 and a sensitivity of 86.17. To our knowledge, this is the first ML model for TNF-α inhibitor prediction. The model is available at http://14.139.57.41/tnfipred/.

Biochemical probing of phloem sap defensive traits in Brassica juncea-B. fruticulosa introgression lines following Lipaphis erysimi infestation.

The lack of resistance to Lipaphis erysimi in cultivated Brassicas makes caused this pest highly devastating resulting in significant loss of rapeseed-mustard productivity in India. B. fruticulosa, a wild crucifer is known as an excellent source of resistance to L. erysimi. Therefore, we planned to assess defense associated biochemical alterations and molecular components of B. juncea-B. fruticulosa ILs to mustard aphid. Phenotypic assessment of ILs on the basis of aphid population per plant (APP) categorized genotypes into resistant (7.15-18.50 APP), moderately susceptible (42.29-53.33 APP) and susceptible (70.00-77.07 APP) genotypes. Mustard aphid infested minimally B. fruticulosa (0.80 APP) among tested genotypes. The maximum increase in catalase (CAT) activity was determined in B. fruticulosa and resistant ILs after 48 h (2.03 and 1.76-fold, respectively) and one week (2.98 and 1.79-fold, respectively) of mustard aphid infestation. The strong induction of CAT2 transcripts (19.25-fold) and CAT activity (5.88-fold) along with low aphid count in resistant IL, Ad4-64 (13.85 APP) suggested the pivotal role of CAT in resistance to mustard aphid. Guaiacol peroxidase (GPX) was significantly decreased following pest infestation at both infestation stages. The ascorbate content was highest in resistant IL, ADV-6RD (2.14-fold) after one week of aphid infestation. H2O2 content rapidly increased in B. juncea-B. fruticulosa derived lines after 48 h of aphid infestation. The negative and significant association between APP and CAT (- 0.56** and - 0.48*, respectively), glutathione (- 0.43* and - 0.40*, respectively), H2O2 (- 0.57** and - 0.43*, respectively) at both 48 h and one week infestation stages signified their role in deterring mustard aphid infestation. The positive and significant association between total sugars (0.33* at 7 DPI), reducing sugars (0.33* at 7 DPI), sucrose (0.36** at 48 h) and APP indicated that higher the sugars content, higher will be mustard aphid infestation in B. juncea derived ILs. The information being generated and key candidates (CAT2, ascorbate and H2O2) being identified may help in effective deployment of B. fruticulosa resistance in mustard breeding.

DNA Methylation and Non-Coding RNAs during Tissue-Injury Associated Pain.

While about half of the population experience persistent pain associated with tissue damages during their lifetime, current symptom-based approaches often fail to reduce such pain to a satisfactory level. To provide better patient care, mechanism-based analgesic approaches must be developed, which necessitates a comprehensive understanding of the nociceptive mechanism leading to tissue injury-associated persistent pain. Epigenetic events leading the altered transcription in the nervous system are pivotal in the maintenance of pain in tissue injury. However, the mechanisms through which those events contribute to the persistence of pain are not fully understood. This review provides a summary and critical evaluation of two epigenetic mechanisms, DNA methylation and non-coding RNA expression, on transcriptional modulation in nociceptive pathways during the development of tissue injury-associated pain. We assess the pre-clinical data and their translational implication and evaluate the potential of controlling DNA methylation and non-coding RNA expression as novel analgesic approaches and/or biomarkers of persistent pain.

SMILES to Smell: Decoding the Structure-Odor Relationship of Chemical Compounds Using the Deep Neural Network Approach.

Finding the relationship between the structure of an odorant molecule and its associated smell has always been an extremely challenging task. The major limitation in establishing the structure-odor relation is the vague and ambiguous nature of the descriptor-labeling, especially when the sources of odorant molecules are different. With the advent of deep networks, data-driven approaches have been substantiated to achieve more accurate linkages between the chemical structure and its smell. In this study, the deep neural network (DNN) with physiochemical properties and molecular fingerprints (PPMF) and the convolution neural network (CNN) with chemical-structure images (IMG) are developed to predict the smells of chemicals using their SMILES notations. A data set of 5185 chemical compounds with 104 smell percepts was used to develop the multilabel prediction models. The accuracies of smell prediction from DNN + PPMF and CNN + IMG (Xception based) were found to be 97.3 and 98.3%, respectively, when applied on an independent test set of chemicals. The deep learning architecture combining both DNN + PPMF and CNN + IMG prediction models is proposed, which classifies smells and may help understand the generic mechanism underlying the relationship between chemical structure and smell perception.

Fluoride and endosulfan together potentiate cytogenetic effects in Swiss albino mice bone marrow cells.

In this study, the cytotoxic potential of fluoride and endosulfan in combination was investigated in Swiss albino mice bone marrow cells using the chromosomal aberration (CA) and micronucleus (MN) test systems. Fluoride (25.1 mg kg-1 body weight [bw] in water) and endosulfan (1.8 mg kg-1 bw by oral intubation) were administered orally alone and in combination (fluoride 25.1 mg kg-1 bw + endosulfan 1.8 mg kg-1 bw) to male Swiss albino mice daily for 30 days. A significant (p < 0.01) increase in micronuclei (MNs) induction and decreased ratio (p < 0.01) of polychromatic to normonochromatic erythrocytes (indicators of cytotoxicity) were observed compared with saline controls when animals were given the combination of fluoride and endosulfan. A significant (p < 0.01) increase in MNs induction and no change in the polychromatic erythrocytes to erythrocyte ratio were also observed when endosulfan was given alone. CAs such as gaps, breaks, fragments, rings, exchanges, and polyploidy were recorded in the bone marrow cells. The mean percent frequency of CAs was increased (p < 0.01) in all the treated groups compared with the control saline group. In the combination group (F + E), the percent frequencies of CAs were significantly higher (13.875%) compared with those in the individual treatment groups of fluoride (4.375%) and endosulfan (6.25%). The mitotic index was calculated as percentage of dividing cells. A significant (p < 0.01) decrease in mitotic index was observed in all treated groups compared with controls. In the combination group (F + E), mitotic index was significantly less than (p < 0.01; 4.1 ± 0.49) the saline control (10.8 ± 0.98). These results indicated that repeated intake of endosulfan through various sources in fluoride affected areas resulted in increased cytotoxic effects. The greater effect in the combination group indicated additive interaction of fluoride and endosulfan in inducing cytotoxicity in Swiss albino mice.

Ophthalmic manifestations in patients with collagen vascular disorders: a hospital-based retrospective observational study.

AIM: To study frequency and characteristics of ocular manifestations in Indian patients with collagen vascular disorders. METHODS: The medical records of 73 patients (Males: Females 16:57) aged between 22 and 78 years (mean ± SD = 43.5 ± 12.9 years) with collagen vascular diseases were analyzed retrospectively for demography, subtypes of collagen vascular disease, and findings of complete ophthalmic examination. RESULTS: Lupus erythematosus (LE) in 39(53.4%, (SLE 18, DLE 21), systemic sclerosis in 27(37%), dermatomyositis in 5(6.8%), and primary Sjögren's syndrome in 2(2.7%) patients, respectively, were observed. Only 35(47.9%) patients had ocular manifestations. In LE keratoconjunctivitis sicca (n = 6), keratitis (n = 5), severe blepharitis (n = 3), retinopathy (n = 2), and optic neuritis in one patient, respectively, were major ocular manifestations. Major abnormalities occurring in systemic sclerosis included restricted eyelid mobility of variable severity (n = 8), eyelid telangiectasia (n = 5), keratoconjunctivitis sicca (n = 6), cataract (n = 5), shallow fornices (n = 4), conjunctival surface disease (n = 4), and uveitis, keratitis, episcleritis in one patient each, respectively. One patient with dermatomyositis had heliotrope rash. Two patients with primary Sjögren's syndrome had keratoconjunctivitis sicca. CONCLUSIONS: The study shows that LE frequently presented with keratoconjunctivitis sicca, retinopathy, and optic neuritis. Systemic sclerosis commonly develops eyelid immobility, blepharitis and telangiectasia, ocular surface disease and keratoconjunctivitis sicca, corneal abnormalities, and uveitis. A comprehensive ocular evaluation is imperative for early detection and management particularly of ocular surface disease, uveitis, and retinopathy to prevent potential sight-threatening complications. Limitations include retrospective study design and small number of patients for stratification.

Synthesis of Core@Shell Cu-Ni@Pt-Cu Nano-Octahedra and Their Improved MOR Activity.

Fabrication of 3d metal-based core@shell nanocatalysts with engineered Pt-surfaces provides an effective approach for improving the catalytic performance. The challenges in such preparation include shape control of the 3d metallic cores and thickness control of the Pt-based shells. Herein, we report a colloidal seed-mediated method to prepare octahedral CuNi@Pt-Cu core@shell nanocrystals using CuNi octahedral cores as the template. By precisely controlling the synthesis conditions including the deposition rate and diffusion rate of the shell-formation through tuning the capping ligand, reaction temperature, and heating rate, uniform Pt-based shells can be achieved with a thickness of <1 nm. The resultant carbon-supported CuNi@Pt-Cu core@shell nano-octahedra showed superior activity in electrochemical methanol oxidation reaction (MOR) compared with the commercial Pt/C catalysts and carbon-supported CuNi@Pt-Cu nano-polyhedron counterparts.

Co-prevalence of human Papillomaviruses (HPV) and Epstein-Barr virus (EBV) in healthy blood donors from diverse nationalities in Qatar.

BACKGROUND: Infections by both human oncoviruses, human Papillomaviruses (HPV) and Epstein-Barr virus (EBV) are very common in the adult human population and are associated with various malignancies. While HPV is generally transmitted sexually or via skin-to-skin contact, EBV is frequently transmitted by oral secretions, blood transfusions and organ transplants. This study aims to determine the prevalence and circulating genotypes of HPV and EBV in healthy blood donors in Qatar. METHODS: We explored the co-prevalence of high-risk HPVs and EBV in 378 males and only 7 females blood donors of different nationalities (mainly from Qatar, Egypt, Syria, Jordan, Pakistan, and India) residing in Qatar, using polymerase chain reaction (PCR). DNA was extracted from the buffy coat and genotyping was performed using PCR and nested-PCR targeting E6 and E7 as well as LMP-1 of HPV and EBV, respectively. RESULTS: We found that from the total number of 385 cases of healthy blood donors studied, 54.8% and 61% of the samples are HPVs and EBV positive, respectively. Additionally, our data revealed that the co-presence of both high-risk HPVs and EBV is 40.4% of the total samples. More significantly, this study pointed out for the first time that the most frequent high-risk HPV types in Qatar are 59 (54.8%), 31 (53.7%), 52 (49.1%), 51 (48.6%), 58 (47%) and 35 (45.5%), while the most commonly expressed low-risk HPV types are 53 (50.6%), 11 (45.5), 73 (41.7%) and 6 (41.3%), with all the cases showing multiple HPVs infection. CONCLUSION: In this study, we demonstrated for the first time that HPV and EBV are commonly co-present in healthy blood donors in Qatar. On the other hand, it is important to highlight that these oncoviruses can also be co-present in several types of human cancers where they can cooperate in the initiation and/or progression of these cancers. Therefore, more studies regarding the co-presence of these oncoviruses and their interaction are necessary to understand their cooperative role in human diseases.

Milk somatic cell derived transcriptome analysis identifies regulatory genes and pathways during lactation in Indian Sahiwal cattle (Bos indicus).

BACKGROUND: The present study is an effort to understand the genomic drivers of lactation in Sahiwal (Bos indicus), the best milch cattle breed of the tropics. METHODS: RNA sequencing of four animals from early, mid and late lactation stages was performed using milk somatic cells as source of RNA. RESULTS: The genes encoding the milk casein and whey proteins showed highest expression in early and mid lactation, with a declining trend towards the late stage. The enhanced expression of PLIN2, FABP5 and FABP3 genes in mid lactation suggests enrichment of the PPARα pathway which is linked to fatty acid metabolism. A gradual decline in the percentage of genes involved in metabolism of proteins, mRNA and insulin synthesis from early to late lactation reflected transition from lactogenesis to involution. Major biological pathways maintained throughout lactation were adaptive immune system, FGF signaling, EGFR signaling, activated TLR4 signaling, NFkB and MAP kinases activation mediated by TLR4 signaling repertoire. Differential expression analysis revealed 547, 1010 and 1313 differentially expressed genes (p < 0.05) between early-late, early-mid and mid-late stages, respectively. The topmost regulatory genes identified by network analysis from the differentially expressed genes, were involved in Chemokine receptor, GPCR and EGFR1 pathways. CONCLUSION: The genes and pathways delineated in this study have regulatory implications in cell morphogenesis, lipid droplet formation and protein synthesis in the course of lactation. The study provides an insight into the expression profile of genes influencing milk properties and lactation in Sahiwal cattle.

Elaeagnus angustifolia Plant Extract Inhibits Epithelial-Mesenchymal Transition and Induces Apoptosis via HER2 Inactivation and JNK Pathway in HER2-Positive Breast Cancer Cells.

Elaeagnus angustifolia (EA) is a medicinal plant used for treating several human diseases in the Middle East. Meanwhile, the outcome of EA extract on HER2-positive breast cancer remains nascent. Thus, we herein investigated the effects of the aqueous EA extract obtained from the flowers of EA on two HER2-positive breast cancer cell lines, SKBR3 and ZR75-1. Our data revealed that EA extract inhibits cell proliferation and deregulates cell-cycle progression of these two cancer cell lines. EA extract also prevents the progression of epithelial-mesenchymal transition (EMT), an important event for cancer invasion and metastasis; this is accompanied by upregulations of E-cadherin and ß-catenin, in addition to downregulations of vimentin and fascin, which are major markers of EMT. Thus, EA extract causes a drastic decrease in cell invasion ability of SKBR3 and ZR75-1 cancer cells. Additionally, we found that EA extract inhibits colony formation of both cell lines in comparison with their matched control. The molecular pathway analysis of HER2 and JNK1/2/3 of EA extract exposed cells revealed that it can block HER2 and JNK1/2/3 activities, which could be the major molecular pathway behind these events. Our findings implicate that EA extract may possess chemo-preventive effects against HER2-positive breast cancer via HER2 inactivation and specifically JNK1/2/3 signaling pathways.

Evolutionary aspects of direct or indirect selection for seed size and seed metabolites in Brassica juncea and diploid progenitor species.

Seed size and seed metabolites have been the targets of direct or indirect selection during domestication and subsequent crop breeding. Understanding these traits and associated genetics can prove very useful for plant translational research. Large germplasm assemblage (235) of Brassica juncea and its progenitor species (B. rapa and B. nigra) was evaluated to establish seed trait variations for seed size and seed metabolites. Seeds were smallest in B. nigra and largest in B. juncea. Australian B. juncea and Indian B. rapa var brown sarson types averaged more seed oil content. Seed size and oil content were generally higher in modern cultivars in comparison to the land races. Allelic diversity for known associated genes for seed-size and oil-content (AP2, ARF2, TTG2, GRF2, GL2, CYP78A5, CYP78A6, MINI3, IKU2, IKU1, BRI1, DGAT, GPDH, LPAAT, GPAT and DA1) was studied so as to infer the effect of domestication on seed traits. Three genes (IKU1, IKU2, AP2) in B. rapa, two (TTG2 and GL2) in B. nigra and two (IKU1 and GRF2) in natural B. juncea were identified as targets of selection on the basis of Fst outlier and/or sequence diversity tests. We report parallel divergence for seed traits between B. juncea and B. rapa. Directional selection appeared stronger for seed-size as compared to correlated seed metabolites. Positive selection on seed-size is likely to have played a significant role in structuring regional variation in the germplasm.