Diversity of endophytic bacteria and fungi in seeds of Elymus nutans growing in four locations of Qinghai Tibet Plateau,China

Plant and Soil - Seeds are involved in the transmission of microorganisms from one plant generation to the next, acting as initial inoculum for the plant microbiome, therefore provide a key source...     

Isolation and partial characterization of an unusual human immunodeficiency retrovirus from two persons of west-central African origin.

An unusual human retrovirus was isolated from two patients with persistent generalized lymphadenopathy who originate from West-Central Africa and are currently residing in Belgium. Although the virus shared a number of the same biological and morphological properties as human immunodeficiency retrovirus type 1 (HIV-1) and HIV-2, significant antigenic differences could be demonstrated. Several of the viral proteins also differed in molecular weight from the corresponding HIV-1 and HIV-2 proteins. Partial chemical cleavage of the most highly conserved viral proteins resulted in patterns which differed from those of HIV-1 and HIV-2. Furthermore, nucleic acid hybridization experiments were capable of discriminating between the virus types. Sequence analysis of the viral U3 region revealed a unique enhancer organization not found in other immunodeficiency viruses. The data indicated that the new isolate is more closely related to HIV-1 than to HIV-2 but clearly differs in a number of important respects.     

Cloning, nucleotide sequence, and expression of the Brucella melitensis bp26 gene coding for a protein immunogenic in infected sheep

Abstract We have previously identified a Brucella melitensis 28 kDa cytosoluble protein (CP28) which was highly immunogenic in infected sheep and which in addition made possible the serological differentiation between infected and B. melitensis Rev.l vaccinated sheep. Monoclonal antibodies against CP28 were used to screen a B. melitensis 16M genomic library and to clone the corresponding gene. DNA sequencing of the gene encoding CP28 of B. melitensis 16M revealed that it was nearly identical to that of the recently published bp26 gene of Brucella abortus vaccine strain S19 coding for a periplasmic protein. The differences between the B. melitensis 16M gene and that of B. abortus S19 consisted of single nucleotide substitutions, one or two codon deletions, one codon addition, and most importantly a 21-bp deletion. The corresponding region of B. abortus S19 contains two 10-bp direct repeats which could have been involved in the genesis of the deletion. Expression of the B. melitensis 16M bp26 gene in Escherichia coli studied by the use of the monoclonal antibodies showed the same characteristics as reported for the B. abortus S19 bp26 gene, i.e. the presence of a higher molecular mass preprotein and a lower molecular mass band which probably corresponds to the mature protein exported to the periplasm. Immunoblotting performed with sera from either naturally infected or B. melitensis H38 experimentally infected sheep confirmed the importance of the B. melitensis CP28/BP26 protein as diagnostic antigen.     

Aptamer-functionalized quantum dots as theranostic nanotools against cancer and bacterial infections: A comprehensive overview of recent trends

Aptamers (Apts) are synthetic nucleic acid ligands that can be engineered to target various molecules, including amino acids, proteins, and pharmaceuticals. Through a series of adsorption, recovery, and amplification steps, Apts are extracted from combinatorial libraries of synthesized nucleic acids. Using aptasensors in bioanalysis and biomedicine can be improved by combining them with nanomaterials. Moreover, Apt-associated nanomaterials, including liposomes, polymeric, dendrimers, carbon nanomaterials, silica, nanorods, magnetic NPs, and quantum dots (QDs), have been widely used as promising nanotools in biomedicine. Following surface modifications and conjugation with appropriate functional groups, these nanomaterials can be successfully used in aptasensing. Advanced biological assays can use Apts immobilized on QD surfaces through physical interaction and chemical bonding. Accordingly, modern QD aptasensing platforms rely on interactions between QDs, Apts, and targets to detect them. QD-Apt conjugates can be used to directly detect prostate, ovarian, colorectal, and lung cancers or simultaneously detect biomarkers associated with these malignancies. Tenascin-C, mucin 1, prostate-specific antigen, prostate-specific membrane antigen, nucleolin, growth factors, and exosomes are among the cancer biomarkers that can be sensitively detected using such bioconjugates. Furthermore, Apt-conjugated QDs have shown great potential for controlling bacterial infections such as Bacillus thuringiensis, Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii, Campylobacter jejuni, Staphylococcus aureus, and Salmonella typhimurium. This comprehensive review discusses recent advancements in the design of QD-Apt bioconjugates and their applications in cancer and bacterial theranostics.     

Identification of marker genes in Alzheimer's disease using a machine-learning model

Alzheimer''s Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.     

Overview of Strain Characterization in Relation to Serological and Molecular Detection of Citrus tristeza <i>Closterovirus</i>

Tristeza is a devastating viral disease in all the citrus growing countries throughout the world and has killed millions of citrus trees in severely affected orchards. The citrus species grafted on sour orange rootstock are affected by this disease. Predominantly, the sweet orange, grapefruit and lime trees grafted on sour orange exhibit severe symptoms like quick decline, vein clearing, pin holing, bark scaling and degeneration leading to variable symptoms. Symptomatic expression of Citrus tristeza virus (CTV) in different hosts has been attributed to virus isolates which are from severe to mild. Different serological and molecular assays have been deployed to differentiate the strains of CTV. Citrus tristeza virus is diversified towards its strains on the basis of biological, serological and molecular characterization. Phenotypic expression is due to genetic alteration and different molecular basis have now been adopted for strain differentiation. This review will give a brief idea about the different CTV isolates, their characterization based on nucleic acid and serological assays. Different methods along with salient features for strain characterization has also been reviewed. This review will also open the new aspects towards formulation of management strategies through different detection techniques.     

Peptides with Dual Antimicrobial-Anticancer Activity Derived from the N-terminal Region of H. pylori Ribosomal Protein L1 (RpL1)

International Journal of Peptide Research and Therapeutics - Whereas the traditional approaches of cancer therapy including radiotherapy, chemotherapy, and immunotherapy have failed to properly...     

In silico studies of urease inhibitors to explore ligand-enzyme interactions

In continuation of our previous study on the urease inhibition by a number of chalcones, 2,3-dihydro-1,5-benzothiazepines and 2,3,4,5-tetrahydro-1,5-benzothiazepines, FlexX docking has been exploited to get a deeper insight into the mechanism of their inhibitory action. A comparison of the IC₅₀ values of the active compounds reveals that, of the three classes of compounds studied, 2,3-dihydro-1,5-benzothiazepines were the most potent urease inhibitors. An in silico examination of these compounds showed that the activity is related to the interaction of ligand with the nickel metallocentre, its interaction with two amino acid residues, Asp224 and Cys322, in addition to the orientation of rings A and B in the catalytic core of the enzyme. The most active compound 2,3-dihydro-1,5-benzothiazepine (4) anchor tightly through a network of interactions with Ni701 and Ni702. This includes a number of hydrogen bonds and hydrophobic contacts with the amino acid residues in its vicinity. For their reduced analogs, the difference in the activity of different diastereomers has been observed to be configuration-dependent. This may be ascribed mainly to the difference in the orientation of ring B of the two stereoisomers and the extent of their interaction with Asp224 and Cys322 present in the catalytic core of the enzyme.     

Mechanistic Phenotypes: An Aggregative Phenotyping Strategy to Identify Disease Mechanisms Using GWAS Data

A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF<0.1) non-synonymous SNPs (nsSNPs) associated with “mechanistic phenotypes”, comprised of collections of related diagnoses. We studied two mechanistic phenotypes: (1) thrombosis, evaluated in a population of 1,655 African Americans; and (2) four groupings of cancer diagnoses, evaluated in 3,009 white European Americans. We tested associations between nsSNPs represented on GWAS platforms and mechanistic phenotypes ascertained from electronic medical records (EMRs), and sought enrichment in functional ontologies across the top-ranked associations. We used a two-step analytic approach whereby nsSNPs were first sorted by the strength of their association with a phenotype. We tested associations using two reverse genetic models and standard additive and recessive models. In the second step, we employed a hypothesis-free ontological enrichment analysis using the sorted nsSNPs to identify functional mechanisms underlying the diagnoses comprising the mechanistic phenotypes. The thrombosis phenotype was solely associated with ontologies related to blood coagulation (Fisher''s p = 0.0001, FDR p = 0.03), driven by the F5, P2RY12 and F2RL2 genes. For the cancer phenotypes, the reverse genetics models were enriched in DNA repair functions (p = 2×10−5, FDR p = 0.03) (POLG/FANCI, SLX4/FANCP, XRCC1, BRCA1, FANCA, CHD1L) while the additive model showed enrichment related to chromatid segregation (p = 4×10−6, FDR p = 0.005) (KIF25, PINX1). We were able to replicate nsSNP associations for POLG/FANCI, BRCA1, FANCA and CHD1L in independent data sets. Mechanism-oriented phenotyping using collections of EMR-derived diagnoses can elucidate fundamental disease mechanisms.