Exploring human-animal host interactions and emergence of COVID-19: Evolutionary and ecological dynamics

The novel coronavirus disease (COVID-19) that emerged in December 2019 had caused substantial morbidity and mortality at the global level within few months. It affected economies, stopped travel, and isolated individuals and populations around the world. Wildlife, especially bats, serve as reservoirs of coronaviruses from which the variant Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) emerged that causes COVID-19. In this review, we describe the current knowledge on COVID-19 and the significance of wildlife hosts in its emergence. Mammalian and avian coronaviruses have diverse host ranges with distinct lineages of coronaviruses. Recombination and reassortments occur more frequently in mixed-animal markets where diverse viral genotypes intermingle. Human coronaviruses have evolved through gene gains and losses primarily in interfaces where wildlife and humans come in frequent contact. There is a gap in our understanding of bats as reservoirs of coronaviruses and there is a misconception that bats periodically transmit coronaviruses to humans. Future research should investigate bat viral diversity and loads at interfaces between humans and bats. Furthermore, there is an urgent need to evaluate viral strains circulating in mixed animal markets, where the coronaviruses circulated before becoming adapted to humans. We propose and discuss a management intervention plan for COVID-19 and raise questions on the suitability of current containment plans. We anticipate that more virulent coronaviruses could emerge unless proper measures are taken to limit interactions between diverse wildlife and humans in wild animal markets.     

Bioelectricity generation using two chamber microbial fuel cell treating wastewater from food processing

Electricity generation from microbial fuel cells which treat food processing wastewater was investigated in this study. Anaerobic anode and aerobic cathode chambers were separated by a proton exchange membrane in a two-compartment MFC reactor. Buffer solutions and food industry wastewater were used as electrolytes in the anode and cathode chambers, respectively. The produced voltage and current intensity were measured using a digital multimeter. Effluents from the anode compartment were tested for COD, BOD₅, NH₃, P, TSS, VSS, SO₄ and alkalinity. The maximum current density and power production were measured 527 mA/m² and 230 mW/m² in the anode area, respectively, at operation organic loading (OLR) of 0.364 g COD/l.d. At OLR of 0.182 g COD/l.d, maximum voltage and columbic efficiency production were recorded 0.475 V and 21%, respectively. Maximum removal efficiency of COD, BOD₅, NH₃, P, TSS, VSS, SO₄ and alkalinity were 86, 79, 73, 18, 68, 62, 30 and 58%, respectively. The results indicated that catalysts and mediator-less microbial fuel cells (CAML-MFC) can be considered as a better choice for simple and complete energy conversion from the wastewater of such industries and also this could be considered as a new method to offset wastewater treatment plant operating costs.     

BMI1, Stem Cell Factor Acting as Novel Serum-biomarker for Caucasian and African-American Prostate Cancer

Background

Lack of reliable predictive biomarkers is a stumbling block in the management of prostate cancer (CaP). Prostate-specific antigen (PSA) widely used in clinics has several caveats as a CaP biomarker. African-American CaP patients have poor prognosis than Caucasians, and notably the serum-PSA does not perform well in this group. Further, some men with low serum-PSA remain unnoticed for CaP until they develop disease. Thus, there is a need to identify a reliable diagnostic and predictive biomarker of CaP. Here, we show that BMI1 stem-cell protein is secretory and could be explored for biomarker use in CaP patients.

Methodology/Principal Findings

Semi-quantitative analysis of BMI1 was performed in prostatic tissues of TRAMP (autochthonous transgenic mouse model), human CaP patients, and in cell-based models representing normal and different CaP phenotypes in African-American and Caucasian men, by employing immunohistochemistry, immunoblotting and Slot-blotting. Quantitative analysis of BMI1 and PSA were performed in blood and culture-media of siRNA-transfected and non-transfected cells by employing ELISA. BMI1 protein is (i) secreted by CaP cells, (ii) increased in the apical region of epithelial cells and stromal region in prostatic tumors, and (iii) detected in human blood. BMI1 is detectable in blood of CaP patients in an order of increasing tumor stage, exhibit a positive correlation with serum-PSA and importantly is detectable in patients which exhibit low serum-PSA. The clinical significance of BMI1 as a biomarker could be ascertained from observation that CaP cells secrete this protein in higher levels than cells representative of benign prostatic hyperplasia (BPH).

Conclusions/Significance

BMI1 could be developed as a dual bio-marker (serum and biopsy) for the diagnosis and prognosis of CaP in Caucasian and African-American men. Though compelling these data warrant further investigation in a cohort of African-American patients.     

Fermentation of glycerol and production of valuable chemical and biofuel molecules

Glycerol has attracted the attention of scientific and industrial communities due to its generation in bulk quantities as a byproduct of biofuel industries. With the rapid growth of these industries in recent years, glycerol is frequently treated as a very low-value byproduct or even a waste product with a disposal cost associated to it. Glycerol is not only abundant and inexpensive but also can generate more reducing equivalents than glucose or xylose. This unique characteristic of glycerol offers a tremendous opportunity for its biological conversion to valuable products at higher yield. This review focuses on research efforts to utilize glycerol as a carbon source for the production of a variety of fuels and chemicals by both native and metabolically engineered microorganisms.     

Modeling and analysis of soybean (Glycine max. L) Cu/Zn,Mn and Fe superoxide dismutases

Superoxide dismutase (SOD, EC 1.15.1.1) is an important metal-containing antioxidant enzyme that provides the first line of defense against toxic superoxide radicals by catalyzing their dismutation to oxygen and hydrogen peroxide. SOD is classified into four metalloprotein isoforms, namely, Cu/Zn SOD, Mn SOD, Ni SOD and Fe SOD. The structural models of soybean SOD isoforms have not yet been solved. In this study, we describe structural models for soybean Cu/Zn SOD, Mn SOD and Fe SOD and provide insights into the molecular function of this metal-binding enzyme in improving tolerance to oxidative stress in plants.     

A model of the evolution of dichogamy incorporating sex-ratio selection, anther-stigma interference, and inbreeding depression

Historically, explanations for the evolution of floral traits that reduce self-fertilization have tended to focus on selection to avoid inbreeding depression. However, there is growing support for the hypothesis that such traits also play a role in promoting efficient pollen dispersal by reducing anther-stigma interference. The relative importance of these two selective pressures is currently a popular topic of investigation. To date, there has been no theoretical exploration of the relative contributions of selection to avoid the genetic costs of self-fertilization and selection to promote efficient pollen dispersal on the evolution of floral traits. We developed a population genetic model to examine the influence of these factors on the evolution of dichogamy: the temporal separation of anther maturation and stigma receptivity. Our analysis indicates that anther-stigma interference can favor dichogamy even in the absence of in-breeding depression. Although anther-stigma interference and inbreeding depression are the key forces driving the initial evolution of dichogamy, selection to match the timing of pollen dispersal to the availability of ovules at the population level becomes a more potent force opposing the further evolution of dichogamy as the extent of temporal separation increases. This result may help to explain otherwise puzzling phenomena such as why dichogamy is rarely complete in nature and why dichogamy tends to be associated with asynchronous flower presentation.     

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be considered as pre-training for various applications of deep learning in bioinformatics. The related data is available at Life Language Processing Website: http://llp.berkeley.edu and Harvard Dataverse: http://dx.doi.org/10.7910/DVN/JMFHTN.     

Phylogenetic Analysis of Selected Menthol-Producing Species Belonging to the Lamiaceae Family

Menthol is an organic compound with diverse medicinal and commercial applications, and is made either synthetically or through extraction from mint oils. The aim of the present study was to investigate menthol levels in selected menthol-producing species belonging to the Lamiaceae family, and to determine phylogenetic relationships of menthol dehydrogenase gene sequence among these species. Three genus of Lamiaceae, namely Mentha, Salvia, and Micromeria, were selected for phytochemical and phylogenetic analyses. After identification of each species based on menthol dehydrogenase gene in NCBI, BLAST software was used for the sequence alignment. MEGA4 software was used to draw phylogenetic tree for various species. Phytochemical analysis revealed that the highest and lowest amounts of both essential oil and menthol belonged to Mentha spicata and Micromeria hyssopifolia, respectively. The species Mentha spicata and Mentha piperita, which were assigned to one cluster in the dendrogram, contained the highest amounts of essential oil and menthol while Micromeria species, which was in the distinct cluster and placed in the farther evolutionary distance, contained the lowest amount of essential oil and menthol. Phylogenetic and phytochemistry analyses showed that essential oil and menthol contents of menthol-producing species are associated with menthol dehydrogenase gene sequence.