Using single cell type proteomics to identify Al-induced proteomes in outer layer cells and interior tissues in the apical meristem/cell division regions of tomato root-tips.

Aluminum (Al) toxicity primarily targets the root tips, inhibiting root growth and function and leading to crop yield losses on acidic soils. Previously we reported using laser capture microdissection (LCM) proteomics to identify Al-induced proteins in the outer layer cells in the transitional zone of tomato root-tips. This study aims to further characterize Al-induced proteomic dynamics from the outer to interior tissues, thus providing a panoramic view reflecting Al resistance in the root tip as a whole in tomatoes. Three types of cells were isolated via LCM from the basal 350-400 µm (below cell elongation regions) of root tips using tomato (Solanum lycopersicum) 'Micro-Tom' plants. Type I and Type II were from Al-treated plants. Type I included cells of the outer three layers, i.e., the epidermis and cortex initials and the quiescent center (QC) in root apical meristem (RAM), and Type II possessed the interior tissues of the same region. Type III contained cells from the non-Al-treated root tips collected in the same region as Type I. Two tandem mass tag (TMT) proteomics analyses with three biological replicates for each sample type were conducted. The TMTexp1 (comparing Type I and Type II) identified 6575 quantifiable proteins and 178 different abundance proteins (DAPs). The TMTexp2 (comparing Type I and Type III) identified 7197 quantifiable proteins and 162 DAPs. Among all quantified proteins (7685) from the two TMT experiments, 6088 (79%) proteins, including 313 DAPs (92% of the 340 total), were identified in all tissues. A model reflecting the tissue-specific Al-resistance mechanism was proposed, in which the level of the citrate transporter MATE protein, involved in Al exclusion, accumulated to the highest level in the outer-layer cells but decreased toward the interior of root-tips (which concurs with the tissue-specific importance in Al resistance). Proteins for biosynthesis of ethylene and jasmonic acid, proteolytic enzymes, stress-responsive proteins, and cell wall modeling were affected by Al treatment, some in a cell type-specific manner. The KEGG metabolite pathways enriched with these DAPs changed depending on the cell types. This study demonstrated the advantage of using the tissue/cell-specific analysis for identifying proteins and their dynamic changes directly associated with Al resistance in the root-tip region. The proteomics datasets have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (https://www.ebi.ac.uk/pride/) with the dataset identifier as PXD021994 under project title: Proteomics studies of outer and inner cellular layers of tomato root-tips for Al stress, Project DOI: 10.6019/ PXD021994; and PXD018234 under Project title: Al-induced root proteomics changes in stress-acclimated tomato plant, Project DOI: https://doi.org/10.6019/PXD018234. SIGNIFICANCE: This paper presents the method of using laser capture microdissection (LCM) to collect homogenous cell-type specific tissue samples from the outer layers and inner central regions of tomato root-tips. The tandem mass tag-proteomics analysis showed that the outer-layer cells expressed proteomes that were different from the inner tissues of Al-treated root-tips; proteins related to resistance/tolerance to Al toxicity were highly accumulated in the outer-layer cells. Furthermore, the Al-treated outer-layer cells expressed proteomes which were different from the non-Al treated counterpart cells. This study has provided the first dataset of proteins differentiating from the outer to inner layers of cells in Al-treated root-tips. It provided convincing experimental evidences demonstrating the single-cell type proteomics as a powerful analytical approach to identify Al tolerance mechanisms in plants. The analytical procedure of LCM-tandem mass tag-quantitative proteomics analysis has a broad application for proteomics analysis of spatially separated cells in complex tissues.

Biochemical Characteristics of Microbial Enzymes and Their Significance from Industrial Perspectives.

Microbes are ubiquitously distributed in nature and are a critical part of the holobiont fitness. They are perceived as the most potential biochemical reservoir of inordinately diverse and multi-functional enzymes. The robust nature of the microbial enzymes with thermostability, pH stability and multi-functionality make them potential candidates for the efficient biotechnological processes under diverse physio-chemical conditions. The need for sustainable solutions to various environmental challenges has further surged the demand for industrial enzymes. Fueled by the recent advent of recombinant DNA technology, genetic engineering, and high-throughput sequencing and omics techniques, numerous microbial enzymes have been developed and further exploited for various industrial and therapeutic applications. Most of the hydrolytic enzymes (protease being the dominant hydrolytic enzyme) have broad range of industrial uses such as food and feed processing, polymer synthesis, production of pharmaceuticals, manufactures of detergents, paper and textiles, and bio-fuel refinery. In this review article, after a short overview of microbial enzymes, an approach has been made to highlight and discuss their potential relevance in biotechnological applications and industrial bio-processes, significant biochemical characteristics of the microbial enzymes, and various tools that are revitalizing the novel enzymes discovery.

An Effective Computational Method Incorporating Multiple Secondary Structure Predictions in Topology Determination for Cryo-EM Images.

A key idea in de novo modeling of a medium-resolution density image obtained from cryo-electron microscopy is to compute the optimal mapping between the secondary structure traces observed in the density image and those predicted on the protein sequence. When secondary structures are not determined precisely, either from the image or from the amino acid sequence of the protein, the computational problem becomes more complex. We present an efficient method that addresses the secondary structure placement problem in presence of multiple secondary structure predictions and computes the optimal mapping. We tested the method using 12 simulated images from α-proteins and two Cryo-EM images of α-ß proteins. We observed that the rank of the true topologies is consistently improved by using multiple secondary structure predictions instead of a single prediction. The results show that the algorithm is robust and works well even when errors/misses in the predicted secondary structures are present in the image or the sequence. The results also show that the algorithm is efficient and is able to handle proteins with as many as 33 helices.

Constrained Cyclic Coordinate Descent for Cryo-EM Images at Medium Resolutions: Beyond the Protein Loop Closure Problem.

The cyclic coordinate descent (CCD) method is a popular loop closure method in protein structure modeling. It is a robotics algorithm originally developed for inverse kinematic applications. We demonstrate an effective method of building the backbone of protein structure models using the principle of CCD and a guiding trace. For medium-resolution 3-dimensional (3D) images derived using cryo-electron microscopy (cryo-EM), it is possible to obtain guiding traces of secondary structures and their skeleton connections. Our new method, constrained cyclic coordinate descent (CCCD), builds α-helices, ß-strands, and loops quickly and fairly accurately along predefined traces. We show that it is possible to build the entire backbone of a protein fairly accurately when the guiding traces are accurate. In a test of 10 proteins, the models constructed using CCCD show an average of 3.91Å of backbone root mean square deviation (RMSD). When the CCCD method is incorporated in a simulated annealing framework to sample possible shift, translation, and rotation freedom, the models built with the true topology were ranked high on the list, with an average backbone RMSD100 of 3.76Å. CCCD is an effective method for modeling atomic structures after secondary structure traces and skeletons are extracted from 3D cryo-EM images.

A machine learning approach for the identification of protein secondary structure elements from electron cryo-microscopy density maps.

The accuracy of the secondary structure element (SSE) identification from volumetric protein density maps is critical for de-novo backbone structure derivation in electron cryo-microscopy (cryoEM). It is still challenging to detect the SSE automatically and accurately from the density maps at medium resolutions (∼5-10 Å). We present a machine learning approach, SSELearner, to automatically identify helices and ß-sheets by using the knowledge from existing volumetric maps in the Electron Microscopy Data Bank. We tested our approach using 10 simulated density maps. The averaged specificity and sensitivity for the helix detection are 94.9% and 95.8%, respectively, and those for the ß-sheet detection are 86.7% and 96.4%, respectively. We have developed a secondary structure annotator, SSID, to predict the helices and ß-strands from the backbone Cα trace. With the help of SSID, we tested our SSELearner using 13 experimentally derived cryo-EM density maps. The machine learning approach shows the specificity and sensitivity of 91.8% and 74.5%, respectively, for the helix detection and 85.2% and 86.5% respectively for the ß-sheet detection in cryoEM maps of Electron Microscopy Data Bank. The reduced detection accuracy reveals the challenges in SSE detection when the cryoEM maps are used instead of the simulated maps. Our results suggest that it is effective to use one cryoEM map for learning to detect the SSE in another cryoEM map of similar quality.