Artificial neural network (ANN)-based algorithms for high light stress phenotyping of tomato genotypes using chlorophyll fluorescence features.

High light (HL) is a common environmental stress directly imposes photoinhibition on the photosynthesis apparatus. Breeding plants for tolerance against HL is therefore highly demanded. Chlorophyll fluorescence (ChlF) is a sensitive indicator of stress in plants and can be evaluated using OJIP transients. In this study, we compared the ChlF features of plants exposed to HL (1200 µmol m-2 s-1) with that of control plants (300 µmol m-2 s-1). To extract the most reliable ChlF features for discrimination between HL-stressed and non-stressed plants, we applied three artificial neural network (ANN)-based algorithms, namely, Boruta, Support Vector Machine (SVM), and Recursive Feature Elimination (RFE). Feature selection algorithms identified multiple features but only two features, namely the maximal quantum yield of PSII photochemistry (FV/FM) and quantum yield of energy dissipation (ɸD0), remained consistent across all genotypes in control conditions, while exhibited variation in HL. Therefore, considered reliable features for HL stress screening. The selected features were then used for screening 14 tomato genotypes for HL. Genotypes were categorized into three groups, tolerant, semi-tolerant, and sensitive genotypes. Foliar hydrogen peroxide (H2O2) and malondialdehyde (MDA) contents were measured as independent proxies for benchmarking selected features. Tolerant genotypes were attributed with the lowest change in H2O2 and MDA contents, while the sensitive genotypes displayed the highest magnitude of increase in H2O2 and MDA by HL treatment compared to the control. Finally, a FV/FM higher than 0.77 and ɸD0 lower than 0.24 indicates a healthy electron transfer chain (ETC) when tomato plants are exposed to HL.

Identification of intelligence-related proteins through a robust two-layer predictor.

In this study, we advance a robust methodology for identifying specific intelligence-related proteins across phyla. Our approach exploits a support vector machine-based classifier capable of predicting intelligence-related proteins based on a pool of meaningful protein features. For the sake of illustration of our proposed general method, we develop a novel computational two-layer predictor, Intell_Pred, to predict query sequences (proteins or transcripts) as intelligence-related or non-intelligence-related proteins or transcripts, subsequently classifying the former sequences into learning and memory-related classes. Based on a five-fold cross-validation and independent blind test, Intell_Pred obtained an average accuracy of 87.48 and 88.89, respectively. Our findings revealed that a score >0.75 (during prediction by Intell_Pred) is a well-grounded choice for predicting intelligence-related candidate proteins in most organisms across biological kingdoms. In particular, we assessed seismonastic movements and associate learning in plants and evaluated the proteins involved using Intell_Pred. Proteins related to seismonastic movement and associate learning showed high percentages of similarities with intelligence-related proteins. Our findings lead us to believe that Intell_Pred can help identify the intelligence-related proteins and their classes using a given protein/transcript sequence.

CaSilico: A versatile CRISPR package for in silico CRISPR RNA designing for Cas12, Cas13, and Cas14.

The efficiency of the CRISPR-Cas system is highly dependent on well-designed CRISPR RNA (crRNA). To facilitate the use of various types of CRISPR-Cas systems, there is a need for the development of computational tools to design crRNAs which cover different CRISPR-Cas systems with off-target analysis capability. Numerous crRNA design tools have been developed, but nearly all of them are dedicated to design crRNA for genome editing. Hence, we developed a tool matching the needs of both beginners and experts, named CaSilico, which was inspired by the limitations of the current crRNA design tools for designing crRNAs for Cas12, Cas13, and Cas14 CRISPR-Cas systems. This tool considers a comprehensive list of the principal rules that are not yet well described to design crRNA for these types. Using a list of important features such as mismatch tolerance rules, self-complementarity, GC content, frequency of cleaving base around the target site, target accessibility, and PFS (protospacer flanking site) or PAM (protospacer adjacent motif) requirement, CaSilico searches all potential crRNAs in a user-input sequence. Considering these features help users to rank all crRNAs for a sequence and make an informed decision about whether a crRNA is suited for an experiment or not. Our tool is sufficiently flexible to tune some key parameters governing the design of crRNA and identification of off-targets, which can lead to an increase in the chances of successful CRISPR-Cas experiments. CaSilico outperforms previous crRNA design tools in the following aspects: 1) supporting any reference genome/gene/transcriptome for which an FASTA file is available; 2) designing crRNAs that simultaneously target multiple sequences through conserved region detection among a set of sequences; 3) considering new CRISPR-Cas subtypes; and 4) reporting a list of different features for each candidate crRNA, which can help the user to select the best one. Given these capabilities, CaSilico addresses end-user concerns arising from the use of sophisticated bioinformatics algorithms and has a wide range of potential research applications in different areas, especially in the design of crRNA for pathogen diagnosis. CaSilico was successfully applied to design crRNAs for different genes in the SARS-CoV-2 genome, as some of the crRNAs have been experimentally tested in the previous studies.

Identification of Gene Modules and Hub Genes Involved in Mastitis Development Using a Systems Biology Approach.

OBJECTIVE: Mastitis is defined as the inflammation of the mammary gland, which impact directly on the production performance and welfare of dairy cattle. Since, mastitis is a multifactorial complex disease and the molecular pathways underlying this disorder have not been clearly understood yet, a system biology approach was used in this study to a better understanding of the molecular mechanisms behind mastitis. METHODS: Publicly available RNA-Seq data containing samples from milk of five infected and five healthy Holstein cows at five time points were retrieved. Gene Co-expression network analysis (WGCNA) approach and functional enrichment analysis were then applied with the aim to find the non-preserved module of genes that their connectivity were altered under infected condition. Hub genes were identified in the non-preserved modules and were subjected to protein-protein interactions (PPI) network construction. RESULTS: Among the 25 modules identified, eight modules were non-preserved and were also biologically associated with inflammation, immune response and mastitis development. Interestingly most of the hub genes in the eight modules were also densely connected in the PPI network. Of the hub genes, 250 genes were hubs in both co-expression and PPI networks and most of them were reported to play important roles in immune response or inflammatory pathways. The blue module was highly enriched in inflammatory responses and STAT1 was suggested to play an important role in mastitis development by regulating the immune related genes in this module. Moreover, a set of highly connected genes were identified such as BIRC3, PSMA6, FYN, F11R, NFKBIZ, NFKBIA, GRO1, PHB, CD3E, IL16, GSN, SOCS2, HCK, VAV1 and TLR6, which have been established to be critical for mastitis pathogenesis. CONCLUSION: This study improved the understanding of the mechanisms underlying bovine mastitis and suggested eight non-preserved modules along with several most important genes with promising potential in etiology of mastitis.