Biosorption and Bioleaching of Heavy Metals from Electronic Waste Varied with Microbial Genera

Industrialization and technological advancements have led to the exploitation of natural resources and the production of hazardous wastes, including electronic waste (E-waste). The traditional physical and chemical techniques used to combat E-waste accumulation have inherent drawbacks, such as the production of harmful gases and toxic by-products. These limitations may be prudently addressed by employing green biological methods, such as biosorption and bioleaching. Therefore, this study was aimed at evaluating the biosorption and bioleaching potential of seven microbial cultures using E-waste (printed circuit board (PCB)) as a substrate under submerged culture conditions. The cut pieces of PCB were incubated with seven microbial cultures in liquid broth conditions in three replicates. Atomic absorption spectroscopy (AAS) analysis of the culture biomass and culture filtrates was performed to evaluate and screen the better-performing microbial cultures for biosorption and bioleaching potentials. The best four cultures were further evaluated through SEM, energy-dispersive X-ray spectroscopy (EDX), and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) studies to identify the possible culture that can be utilized for the biological decontamination of E-waste. The study revealed the highest and differential ability of Pleurotus florida and Pseudomonas spp. for biosorption and bioleaching of copper and iron. This can be attributed to bio-catalysis by the laccase enzyme. For both P. florida and Pseudomonas spp. on the 20th day of incubation, laccase exhibited higher specific activity (6.98 U/mg and 5.98 U/mg, respectively) than other microbial cultures. The biomass loaded with Cu2+ and Fe2+ ions after biosorption was used for the desorption process for recovery. The test cultures exhibited variable copper recovery efficiencies varying between 10.5 and 18.0%. Protein characterization through SDS-PAGE of four promising microbial cultures exhibited a higher number of bands in E-waste as compared with microbial cultures without E-waste. The surface topography studies of the E-waste substrate showed etching, as well as deposition of vegetative and spore cells on the surfaces of PCB cards. The EDX studies of the E-waste showed decreases in metal element content (% wt/% atom basis) on microbial treatment from the respective initial concentrations present in non-treated samples, which established the bioleaching phenomenon. Therefore, these microbial cultures can be utilized to develop a biological remediation method to manage E-waste.

Sequence Coverage Visualizer: A web application for protein sequence coverage 3D visualization

Protein structure is connected with its function and interaction and plays an extremely important role in protein characterization. As one of the most important analytical methods for protein characterization, Proteomics is widely used to determine protein composition, quantitation, interaction, and even structures. However, due to the gap between identified proteins by proteomics and available 3D structures, it was very challenging, if not impossible, to visualize proteomics results in 3D and further explore the structural aspects of proteomics experiments. Recently, two groups of researchers from DeepMind and Baker lab have independently published protein structure prediction tools that can help us obtain predicted protein structures for the whole human proteome. Although there is still debate on the validity of some of the predicted structures, it is no doubt that these represent the most accurate predictions to date. More importantly, this enabled us to visualize the majority of human proteins for the first time. To help other researchers best utilize these protein structure predictions, we present the Sequence Coverage Visualizer (SCV), http://scv.lab.gy, a web application for protein sequence coverage 3D visualization. Here we showed a few possible usages of the SCV, including the labeling of post-translational modifications and isotope labeling experiments. These results highlight the usefulness of such 3D visualization for proteomics experiments and how SCV can turn a regular result list into structural insights. Furthermore, when used together with limited proteolysis, we demonstrated that SCV can help validate and compare different protein structures, including predicted ones and existing PDB entries. By performing limited proteolysis on native proteins at various time points, SCV can visualize the progress of the digestion. This time-series data further allowed us to compare the predicted structure and existing PDB entries. Although not deterministic, these comparisons could be used to refine current predictions further and represent an important step towards a complete and correct protein structure database. Overall, SCV is a convenient and powerful tool for visualizing proteomics results.

Characterization of cassava ORANGE proteins and their capability to increase provitamin A carotenoids accumulation

Cassava (Manihot esculenta Crantz) biofortification with provitamin A carotenoids is an ongoing process that aims to alleviate vitamin A deficiency. The moderate content of provitamin A carotenoids achieved so far limits the contribution to providing adequate dietary vitamin A levels. Strategies to increase carotenoid content focused on genes from the carotenoids biosynthesis pathway. In recent years, special emphasis was given to ORANGE protein (OR), which promotes the accumulation of carotenoids and their stability in several plants. The aim of this work was to identify, characterize and investigate the role of OR in the biosynthesis and stabilization of carotenoids in cassava and its relationship with phytoene synthase (PSY), the rate-limiting enzyme of the carotenoids biosynthesis pathway. Gene and protein characterization of OR, expression levels, protein amounts and carotenoids levels were evaluated in roots of one white (60444) and two yellow cassava cultivars (GM5309-57 and GM3736-37). Four OR variants were found in yellow cassava roots. Although comparable expression was found for three variants, significantly higher OR protein amounts were observed in the yellow varieties. In contrast, cassava PSY1 expression was significantly higher in the yellow cultivars, but PSY protein amount did not vary. Furthermore, we evaluated whether expression of one of the variants, MeOR_X1, affected carotenoid accumulation in cassava Friable Embryogenic Callus (FEC). Overexpression of maize PSY1 alone resulted in carotenoids accumulation and induced crystal formation. Co-expression with MeOR_X1 led to greatly increase of carotenoids although PSY1 expression was high in the co-expressed FEC. Our data suggest that posttranslational mechanisms controlling OR and PSY protein stability contribute to higher carotenoid levels in yellow cassava. Moreover, we showed that cassava FEC can be used to study the efficiency of single and combinatorial gene expression in increasing the carotenoid content prior to its application for the generation of biofortified cassava with enhanced carotenoids levels.

DeepLRR: An Online Webserver for Leucine-Rich-Repeat Containing Protein Characterization Based on Deep Learning

Members of the leucine-rich repeat (LRR) superfamily play critical roles in multiple biological processes. As the LRR unit sequence is highly variable, accurately predicting the number and location of LRR units in proteins is a highly challenging task in the field of bioinformatics. Existing methods still need to be improved, especially when it comes to similarity-based methods. We introduce our DeepLRR method based on a convolutional neural network (CNN) model and LRR features to predict the number and location of LRR units in proteins. We compared DeepLRR with six existing methods using a dataset containing 572 LRR proteins and it outperformed all of them when it comes to overall F1 score. In addition, DeepLRR has integrated identifying plant disease-resistance proteins (NLR, LRR-RLK, LRR-RLP) and non-canonical domains. With DeepLRR, 223, 191 and 183 LRR-RLK genes in Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa ssp. Japonica) and tomato (Solanum lycopersicum) genomes were re-annotated, respectively. Chromosome mapping and gene cluster analysis revealed that 24.2% (54/223), 29.8% (57/191) and 16.9% (31/183) of LRR-RLK genes formed gene cluster structures in Arabidopsis, rice and tomato, respectively. Finally, we explored the evolutionary relationship and domain composition of LRR-RLK genes in each plant and distributions of known receptor and co-receptor pairs. This provides a new perspective for the identification of potential receptors and co-receptors.

Disrupting ATXN1 Nuclear Localization in a Knock-in SCA1 Mouse Model Improves a Spectrum of SCA1-Like Phenotypes and their Brain Region Associated Transcriptomic Profiles

Spinocerebellar ataxia type 1 (SCA1) is a dominant trinucleotide repeat neurodegenerative disease characterized by motor dysfunction, cognitive impairment, and premature death. Degeneration of cerebellar Purkinje cells is a frequent and prominent pathological feature of SCA1. We previously showed that transport of ATXN1 to Purkinje cell nuclei is required for pathology, where mutant ATXN1 alters transcription. To examine the role of ATXN1 nuclear localization broadly in SCA1-like disease pathogenesis, CRISPR-Cas9 was used to develop a mouse with the amino acid alteration (K772T) in the nuclear localization sequence of the expanded ATXN1 protein. Characterization of these mice indicates proper nuclear localization of mutant ATXN1 contributes to many disease-like phenotypes including motor dysfunction, cognitive deficits, and premature lethality. RNA sequencing analysis of genes whose expression was corrected to WT levels in Atxn1175QK772T/2Q mice indicates that transcriptomic aspects of SCA1 pathogenesis differ between the cerebellum, brainstem, cerebral cortex, hippocampus, and striatum.

REVOLVER: a low-cost automated protein purifier based on parallel preparative gravity column workflows

Protein purification is a ubiquitous operation in biochemistry and life sciences and represents a key step to producing purified proteins for research (understanding how proteins work) and various applications. The need for scalable and parallel protein purification systems keeps growing due to the increase in throughput in the production of recombinant proteins and in the ever-growing scale of biochemistry research. Therefore, automating the process to handle multiple samples in parallel with minimal human intervention is highly desirable; yet only a handful of such tools have been developed, all of which are closed source and expensive. To address this challenge, we present REVOLVER, a 3D-printed programmable and automatic protein purification system based on gravity-column workflows and controlled by Arduino boards that can be built for under $130 USD. REVOLVER completes a full protein purification process with almost no human intervention and yields results equivalent to those obtained by an experienced biochemist when purifying a real-world protein sample. We further present and describe MULTI-VOLVER, a scalable version of the REVOLVER that allows for parallel purification of up to six samples and can be built for under $250 USD. Both systems will be useful to accelerate protein purification and ultimately link them to bio-foundries for protein characterization and engineering.

Survival Comes at a Cost: A Coevolution of Phage and Its Host Leads to Phage Resistance and Antibiotic Sensitivity of Pseudomonas aeruginosa Multidrug Resistant Strains

The increasing ineffectiveness of traditional antibiotics and the rise of multidrug resistant (MDR) bacteria have necessitated the revival of bacteriophage (phage) therapy. However, bacteria might also evolve resistance against phages. Phages and their bacterial hosts coexist in nature, resulting in a continuous coevolutionary competition for survival. We have isolated several clinical strains of Pseudomonas aeruginosa and phages that infect them. Among these, the PIAS (Phage Induced Antibiotic Sensitivity) phage belonging to the Myoviridae family can induce multistep genomic deletion in drug-resistant clinical strains of P. aeruginosa, producing a compromised drug efflux system in the bacterial host. We identified two types of mutant lines in the process: green mutants with SNPs (single nucleotide polymorphisms) and smaller deletions and brown mutants with large (∼250 kbp) genomic deletion. We demonstrated that PIAS used the MexXY-OprM system to initiate the infection. P. aeruginosa clogged PIAS phage infection by either modifying or deleting these receptors. The green mutant gaining phage resistance by SNPs could be overcome by evolved PIASs (E-PIASs) with a mutation in its tail-fiber protein. Characterization of the mutant phages will provide a deeper understanding of phage-host interaction. The coevolutionary process continued with large deletions in the same regions of the bacterial genomes to block the (E-)PIAS infection. These mutants gained phage resistance via either complete loss or substantial modifications of the phage receptor, MexXY-OprM, negating its essential role in antibiotic resistance. In vitro and in vivo studies indicated that combined use of PIAS and antibiotics could effectively inhibit P. aeruginosa growth. The phage can either eradicate bacteria or induce antibiotic sensitivity in MDR-resistant clinical strains. We have explored the potential use of combination therapy as an alternative approach against MDR P. aeruginosa infection.

Development of Methodology to Investigate the Surface SMALPome of Mammalian Cells

Extraction of membrane proteins from biological membranes has traditionally involved detergents. In the past decade, a new technique has been developed, which uses styrene maleic acid (SMA) copolymers to extract membrane proteins into nanodiscs without the requirement of detergents. SMA nanodiscs are compatible with analytical techniques, such as small-angle scattering, NMR spectroscopy, and DLS, and are therefore an attractive medium for membrane protein characterization. While mass spectrometry has also been reported as a technique compatible with copolymer extraction, most studies have focused on lipidomics, which involves solvent extraction of lipids from nanodiscs prior to mass-spectrometry analysis. In this study, mass spectrometry proteomics was used to investigate whether there are qualitative or quantitative differences in the mammalian plasma membrane proteins extracted with SMA compared to a detergent control. For this, cell surface proteins of 3T3L1 fibroblasts were biotinylated and extracted using either SMA or detergent. Following affinity pull-down of biotinylated proteins with NeutrAvidin beads, samples were analyzed by nanoLC-MS. Here, we report for the first time, a global proteomics protocol for detection of a mammalian cell “SMALPome”, membrane proteins incorporated into SMA nanodiscs. Removal of SMA from samples prior to processing of samples for mass spectrometry was a crucial step in the protocol. The reported surface SMALPome of 3T3L1 fibroblasts consists of 205 integral membrane proteins. It is apparent that the detergent extraction method used is, in general, quantitatively more efficient at extracting proteins from the plasma membrane than SMA extraction. However, samples prepared following detergent extraction contained a greater proportion of proteins that were considered to be “non-specific” than in samples prepared from SMA extracts. Tantalizingly, it was also observed that proteins detected uniquely or highly preferentially in pull-downs from SMA extracts were primarily multi-spanning membrane proteins. These observations hint at qualitative differences between SMA and detergent extraction that are worthy of further investigation.