From Natural Microbe Screening to Sustained Chitinase Activity in Exogenous Hosts.

Genetic parts and hosts can be sourced from nature to realize new functions for synthetic biology or to improve performance in a particular application environment. Here, we proceed from the discovery and characterization of new parts to stable expression in new hosts with a particular focus on achieving sustained chitinase activity. Chitinase is a key enzyme for various industrial applications that require the breakdown of chitin, the second most abundant biopolymer on the earth. Diverse microbes exhibit chitinase activity, but for applications, the environmental conditions for optimal enzyme activity and microbe fitness must align with the application context. Achieving sustained chitinase activity under broad conditions in heterologous hosts has also proven difficult due to toxic side effects. Toward addressing these challenges, we first screen ocean water samples to identify microbes with chitinase activity. Next, we perform whole genome sequencing and analysis and select a chitinase gene for heterologous expression. Then, we optimize transformation methods for target hosts and introduce chitinase. Finally, to achieve robust function, we optimize ribosome binding sites and discover a beneficial promoter that upregulates chitinase expression in the presence of colloidal chitin in a sense-and-respond fashion. We demonstrate chitinase activity for >21 days in standard (Escherichia coli) and nonstandard (Roseobacter denitrificans) hosts. Besides enhancing chitinase applications, our pipeline is extendable to other functions, identifies natural microbes that can be used directly in non-GMO contexts, generates new parts for synthetic biology, and achieves weeks of stable activity in heterologous hosts.

Potential use of endemic human coronaviruses to stimulate immunity against pathogenic SARS-CoV-2 and its variants.

While severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes significant morbidity and mortality in humans, there is a wide range of disease outcomes following virus exposures. Some individuals are asymptomatic while others develop complications within a few days after infection that can lead to fatalities in a smaller portion of the population. In the present study, we have analyzed the factors that may influence the outcome of post-SARS-CoV-2 infection. One factor that may influence virus control is pre-existing immunity conferred by an individual's past exposures to endemic coronaviruses (eCOVIDs) which cause the common cold in humans and generally, most children are exposed to one of the four eCOVIDs before 2 years of age. Here, we have carried out protein sequence analyses to show the amino acid homologies between the four eCOVIDs (i.e. OC43, HKU1, 229E, and NL63) as well as examining the cross-reactive immune responses between SARS-CoV-2 and eCOVIDs by epidemiologic analyses. Our results show that the nations where continuous exposures to eCOVIDs are very high due to religious and traditional causes showed significantly lower cases and low mortality rates per 100,000. We hypothesize that in the areas of the globe where Muslims are in majority and due to religious practices are regularly exposed to eCOVIDs they show a significantly lower infection, as well as mortality rate, and that is due to pre-existing cross-immunity against SARS-CoV-2. This is due to cross-reactive antibodies and T-cells that recognize SARS-CoV-2 antigens. We also have reviewed the current literature that has also proposed that human infections with eCOVIDs impart protection against disease caused by subsequent exposure to SARS-CoV-2. We propose that a nasal spray vaccine consisting of selected genes of eCOVIDs would be beneficial against SARS-CoV-2 and other pathogenic coronaviruses.

Differential expression of miRNAs in a human developing neuronal cell line chronically infected with Zika virus.

Zika virus (ZIKV) is a serious public health concern that may lead to neurological disorders in affected individuals. The virus can be transmitted from an infected mother to her fetus, via mosquitoes, or sexually. ZIKV infections are associated with increased risk for Guillain-Barré syndrome (GBS) and congenital microcephaly in newborns infected prenatally. Dysregulations of intracellular microRNAs (miRNAs) in infected neurons have been linked to different neurological diseases. To determine the potential role of miRNAs in ZIKV infection we developed a chronically infected neuroblastoma cell line and carried out differential expression analyses of miRNAs with reference to an uninfected neuroblastoma cell line. A total of 3192miRNAs were evaluated and 389 were found to be upregulated < 2-fold and 1291 were downregulated < 2-fold. In particular, we determined that hsa-mir-431-5p, hsa-mir-3687, hsa-mir-4655-5p, hsa-mir-6071, hsa-mir-762, hsa-mir-5787, and hsa-mir-6825-3p were significantly downregulated, ranging from -5711 to -660-fold whereas, has-mir-4315, hsa-mir-5681b, hsa-mir-6511a-3p, hsa-mir-1264, hsa-mir-4418, hsa-mir-4497, hsa-mir-4485-3p, hsa-mir-4715-3p, hsa-mir-4433-3p, hsa-mir-4708-3p, hsa-mir-1973 and hsa-mir-564 were upregulated, ranging from 20-0.8-fold. We carried out target gene alignment of these miRNAs with the ZIKV genome to predict the function of the differentially expressed miRNAs and their potential impact on ZIKV pathogenesis. These miRNAs might prove useful as novel diagnostic or therapeutic markers and targets for further research on ZIKV infection and neuronal injury resulting from ZIKV infectivity in developing fetal brain neurons.

Heavy metal pollution promotes antibiotic resistance potential in the aquatic environment.

Water pollution is one of the main challenges and water crises, which has caused the existing water resources to be unusable due to contamination. To understand the determinants of the distribution and abundance of antibiotic resistance genes (ARGs), we examined the distribution of 22 ARGs in relation to habitat type, heavy metal pollution and antibiotics concentration across six lakes and wetlands of Iran. The concentration of 13 heavy metals was determined by inductively coupled plasma atomic emission spectroscopy (ICP-AES) by Thermo Electron Corporation, and five antibiotics by online enrichment and triple-quadrupole LC-MS/MS were investigated. We further performed a global meta-analysis to evaluate the distribution of ARGs across global lakes compared with our studied lakes. While habitat type effect was negligible, we found a strong correlation between waste discharge into the lakes and the abundance of ARGs. The ARGs abundance showed stronger correlation with the concentration of heavy metals, such as Vanadium, than with that of antibiotics. Our meta-analysis also confirmed that overuse of antibiotics and discharge of heavy metals in the studied lakes. These data point to an increase in the distribution of ARGs among bacteria and their increasing resistance to various antibiotics, implying the susceptibility of aquatic environment to industrial pollution.

Phytosynthesis of Cu/rGO using Euphorbia cheiradenia Boiss extract and study of its ability in the reduction of organic dyes and 4-nitrophenol in aqueous medium.

For the first time, copper nanoparticles (Cu NPs) superficially deposited on reduced graphene oxide (rGO) using Euphorbia cheiradenia Boiss leaf aqueous media. A beneficial series of analytical methods was used to characterise E. cheiradenia Boiss leaf extract and involved nanostructures. The Cu/rGO nanocomposite (NC) obtained from the conversion of Cu2+ ions to Cu NPs and GO to rGO undergoes the plant extract and used as a heterogeneous and reusable nanocatalyst for the destruction of 4-nitrophenol, rhodamine B, methylene blue, methyl orange and congo red using sodium borohydride at ambient temperature. In addition, Cu/rGO NC has reusability for many times in the reduction reactions with no decreasing of its catalytic capability.

In silico prediction of long intergenic non-coding RNAs in sheep.

Long non-coding RNAs (lncRNAs) are transcribed RNA molecules >200 nucleotides in length that do not encode proteins and serve as key regulators of diverse biological processes. Recently, thousands of long intergenic non-coding RNAs (lincRNAs), a type of lncRNAs, have been identified in mammalians using massive parallel large sequencing technologies. The availability of the genome sequence of sheep (Ovis aries) has allowed us genomic prediction of non-coding RNAs. This is the first study to identify lincRNAs using RNA-seq data of eight different tissues of sheep, including brain, heart, kidney, liver, lung, ovary, skin, and white adipose. A computational pipeline was employed to characterize 325 putative lincRNAs with high confidence from eight important tissues of sheep using different criteria such as GC content, exon number, gene length, co-expression analysis, stability, and tissue-specific scores. Sixty-four putative lincRNAs displayed tissues-specific expression. The highest number of tissues-specific lincRNAs was found in skin and brain. All novel lincRNAs that aligned to the human and mouse lincRNAs had conserved synteny. These closest protein-coding genes were enriched in 11 significant GO terms such as limb development, appendage development, striated muscle tissue development, and multicellular organismal development. The findings reported here have important implications for the study of sheep genome.

Understanding the undelaying mechanism of HA-subtyping in the level of physic-chemical characteristics of protein.

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.

Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models.

Rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important in diagnosis of this disease. Furthermore sequence-derived structural and physicochemical descriptors are very useful for machine learning prediction of protein structural and functional classes, classifying proteins and the prediction performance. Herein, in this study is the classification of lung tumors based on 1497 attributes derived from structural and physicochemical properties of protein sequences (based on genes defined by microarray analysis) investigated through a combination of attribute weighting, supervised and unsupervised clustering algorithms. Eighty percent of the weighting methods selected features such as autocorrelation, dipeptide composition and distribution of hydrophobicity as the most important protein attributes in classification of SCLC, NSCLC and COMMON classes of lung tumors. The same results were observed by most tree induction algorithms while descriptors of hydrophobicity distribution were high in protein sequences COMMON in both groups and distribution of charge in these proteins was very low; showing COMMON proteins were very hydrophobic. Furthermore, compositions of polar dipeptide in SCLC proteins were higher than NSCLC proteins. Some clustering models (alone or in combination with attribute weighting algorithms) were able to nearly classify SCLC and NSCLC proteins. Random Forest tree induction algorithm, calculated on leaves one-out and 10-fold cross validation) shows more than 86% accuracy in clustering and predicting three different lung cancer tumors. Here for the first time the application of data mining tools to effectively classify three classes of lung cancer tumors regarding the importance of dipeptide composition, autocorrelation and distribution descriptor has been reported.

Correction: Classification of Lung Cancer Tumors Based on Structural and Physicochemical Properties of Proteins by Bioinformatics Models.

[This corrects the article DOI: 10.1371/journal.pone.0040017.].

Are there any differences between features of proteins expressed in malignant and benign breast cancers?

BACKGROUND: The most common cancer among women is breast cancer and it has been blamed as the second leading cause of cancer death in women; so far many approaches have been used to analyze and detect benign and malignant forms of cancer and understanding the features involved in proteins expressed by various types of breast cancers is crucial. METHODS: Herein features of proteins expressed in malignant, benign and both cancers were compared using different screening techniques, clustering methods, decision tree models and generalized rule induction (GRI) algorithms to look for patterns of similarity in two benign and malignant breast cancer groups. RESULTS: The findings showed that the N-terminal amino acid was Met and 57 out of 838 proteins' features ranked as important (p > 0.05). The depth of the trees induced by tree induction models varied from 5 (in the Quest model) to 2 (in the C5.0 model) branches. The best performance evaluation found when C&RT model applied and the worst evaluation found when CHAID model applied. No significant difference in the percentage of correctness, performance evaluation, and mean correctness in tree induction algorithms was found when feature selection applied on datasets, but the number of peer groups reduced significantly (p < 0.05) when feature selection model applied. CONCLUSIONS: The frequency of Ile-Ile was the most important protein attributes in all tree and rule induction models. The importance of sequence-based classification and the frequency of Ile-Ile in prediction of malignant and benign breast cancer have been discussed here.