ATP6V1B2 and IFI27 and their intrinsic functional genomic characteristics associated with SARS-CoV-2

Genes functionally associated with SARS-CoV-2 and genes functionally related to COVID-19 disease can be different, whose distinction will become the first essential step for successfully fighting against the COVID-19 pandemic. Unfortunately, this first step has not been completed in all biological and medical research. Using a newly developed max-competing logistic classifier, two genes, ATP6V1B2 and IFI27, stand out to be critical in transcriptional response to SARS-CoV-2 with differential expressions derived from NP/OP swab PCR. This finding is evidenced by combining these two genes with one another gene in predicting disease status to achieve better-indicating power than existing classifiers with the same number of genes. In addition, combining these two genes with three other genes to form a five-gene classifier outperforms existing classifiers with ten or more genes. With their exceptional predicting power, these two genes can be critical in fighting against the COVID-19 pandemic as a new focus and direction. Comparing the functional effects of these genes with a five-gene classifier with 100% accuracy identified and tested from blood samples in the literature, genes and their transcriptional response and functional effects to SARS-CoV-2 and genes and their functional signature patterns to COVID-19 antibody are significantly different, which can be interpreted as the former is the point of a phenomenon, and the latter is the essence of the disease. Such significant findings can help explore the causal and pathological clue between SARS-CoV-2 and COVID-19 disease and fight against the disease with more targeted vaccines, antiviral drugs, and therapies.

Genetic and QTL Analysis for Hilum-Eye Types in Cowpea (Vigna Unguiculata L. Walp)

Cowpea is an important food legume widely grown in the semi-arid tropics and serves as a main source of dietary protein, minerals, and vitamins. However, varieties differ from region to region based on the consumer’s preference for seed types determined by seed size, seed coat texture, seed color, and hilum-eye types. The genetics of seed size, seed color, and seed coat texture have been well documented, but the hilum-eye types have not been studied well because they represent seven different types with complex interactions. We studied the genetic segregation for hilum-eye types and determined the number of genes involved in a recombinant inbred line (RIL) population derived from a cross between a small eye parent ‘GEC’ and a Watson eye parent ‘IT98K-476-8’. The results demonstrated a three-gene model, W (Watson), S (small), and R (large), for cowpea seed hilum-eye type pattern and the interaction of these three genes, W, S, and R, resulted in five phenotypes, viz. self, Watson, small, large, and ring hilum-eye types. Moreover, we also mapped the RILs for hilum-eye types, identified three quantitative trait loci (QTLs), and aligned to the cowpea reference genome as QTL qHilum7.1, qHilum9.1, and qHilum10.1, corresponding to these three genes, Ring type (R), Watson type (W), and Small type (S) hilum-eye type patterns, respectively. Therefore, there was a complete agreement between the genetic analysis and QTL mapping for the number of genes controlling the hilum types in cowpea.

The exit of axons and glial membrane from the developing Drosophila retina requires integrins

Coordinated development of neurons and glia is essential for the establishment of neuronal circuits during embryonic development. In the developing Drosophila visual system, photoreceptor (R cell) axons and wrapping glial (WG) membrane extend from the eye disc through the optic stalk into the optic lobe. Extensive studies have identified a number of genes that control the establishment of R-cell axonal projection pattern in the optic lobe. The molecular mechanisms directing the exit of R-cell axons and WG membrane from the eye disc, however, remain unknown. In this study, we show that integrins are required in R cells for the extension of R-cell axons and WG membrane from the eye disc into the optic stalk. Knockdown of integrins in R cells but not WG caused the stalling of both R-cell axons and WG membrane in the eye disc. Interfering with the function of Rhea (i.e. the Drosophila ortholog of vertebrate talin and a key player of integrin-mediated adhesion), caused an identical stalling phenotype. These results support a key role for integrins on R-cell axons in directing R-cell axons and WG membrane to exit the eye disc.

Genomic Machine Learning Model Predicts Radiation Therapy Benefit in Early-Stage Breast Cancer Patients with High Accuracy

BackgroundRadiation therapy (RT) is frequently recommended for post-surgery treatment of early-stage breast cancer (BC) patients, though not all benefit. Clinical factors currently guide RT treatment decisions. At present, models to predict RT-benefit predominantly use statistical methods with modest performance. In this paper we present a high-accuracy genomic Machine Learning (ML) model to predict RT-benefit in early-stage BC patients. We also present a novel method for selecting genomic features for training ML algorithms. MethodsGene expression data from 463 early-stage BC patients treated with surgery and RT from the METABRIC cohort were obtained. Wilcoxon Rank Sum (Wilcoxon RS) test and Cox Proportional Hazards (Cox PH) were used to reduce the number of genes used to train eight ML algorithms. ML algorithms were trained on 80% of data using 10-fold cross validation and tested on 20% of data to assess performance in predicting relapse status. Results Genome-wide gene expression data was reduced by 96% using Wilcoxon RS and Cox PH to a 1,596 gene set and a 977 gene set. These gene sets were used to train eight ML algorithms resulting in models that ranged in performance accuracies from 54.01% to 95.6%. Highest accuracies were obtained using Support Vector Machine (SVM977–93.41%, SVM1596–95.6%) and Neural Networks algorithms (NN977 – 92.31%, NN1596 – 93.41%). In RT-untreated patients, accuracies of all models were 30% to 40% lower compared to RT-treated patients. SVM977 had the highest sensitivity of 91.09%. Members of the 977 set were enriched with genes involved in cell cycle and differentiation as well as genes associated with radiosensitivity and radioresistance. Conclusion This study presents a novel genomic feature selection approach that used Wilcoxon RS followed by Cox PH to reduce the number of genes from genome-wide gene expression data used for training ML algorithms by 96%. This approach led to an SVM model that used the expression values of 977 genes to predict RT-benefit in early-stage BC patients with 93.41% accuracy. This work demonstrates that ML models can be clinically useful for predicting cancer patient outcomes.

Understanding Transcriptomic and Serological Differences between Forced Molting and Natural Molting in Laying Hens

Molting is natural adaptation to climate change in all birds, including chickens. Forced molting (FM) can rejuvenate and reactivate the reproductive potential of aged hens, but the effect of natural molting (NM) on older chickens is not clear. To explore why FM has a dramatically different effect on chickens compared with NM, the transcriptome analyses of the hypothalamus and ovary in forced molted and natural molted hens at two periods with feathers fallen and regrown were performed. Additionally, each experimental chicken was tested for serological indices. The results of serological indices showed that growth hormone, thyroid stimulating hormone, and thyroxine levels were significantly higher (p < 0.05) in forced molted hens than in natural molted hens, and calcitonin concentrations were lower in the forced molted than in the natural molted hens. Furthermore, the transcriptomic analysis revealed a large number of genes related to disease resistance and anti-aging in the two different FM and NM periods. These regulatory genes and serological indices promote reproductive function during FM. This study systematically revealed the transcriptomic and serological differences between FM and NM, which could broaden our understanding of aging, rejuvenation, egg production, and welfare issues related to FM in chickens.

Asso approach for classifying gene expression data based on optimal features

Gene expression profiles are sequences of numbers, and the need to analyze them has now increased significantly. Gene expression data contain a large number of genes and models used for cancer classification. As the wealth of these data being produced, new prediction, classification and clustering techniques are applied to the analysis of the data. Although there are a number of proposed methods with good results, there is still limited diagnostics and a lot of problems still to be solved. To solve the difficulty, in this paper, an efficient gene expression data classification is proposed. To predict the cancer class of patients from the gene expression profile, this paper presents a novel classification framework in the manner of three steps namely, Pre-processing, feature selection and classification. In pre-processing, missing value is filled and redundant data are removed. To attain the enhanced classification outcomes, the important features are selected from the database with the help of Adaptive Salp Swarm Optimization (ASSO) algorithm. Then, the selected features are given to the multi kernel SVM (MKSVM) to classify the gene expression data namely, BRCA, KIRC, COAD, LUAD and PRAD. The performance of proposed methodology is analyzed in terms of different metrics namely, accuracy, sensitivity and specificity. The performance of proposed methodology is 4.5% better than existing method in terms of accuracy.

Knockout of Arabidopsis thaliana VEP1, Encoding a PRISE (Progesterone 5β-Reductase/Iridoid Synthase-Like Enzyme), Leads to Metabolic Changes in Response to Exogenous Methyl Vinyl Ketone (MVK)

Small or specialized natural products (SNAPs) produced by plants vary greatly in structure and function, leading to selective advantages during evolution. With a limited number of genes available, a high promiscuity of the enzymes involved allows the generation of a broad range of SNAPs in complex metabolic networks. Comparative metabolic studies may help to understand why—or why not—certain SNAPs are produced in plants. Here, we used the wound-induced, vein patterning regulating VEP1 (AtStR1, At4g24220) and its paralogue gene on locus At5g58750 (AtStR2) from Arabidopsis to study this issue. The enzymes encoded by VEP1-like genes were clustered under the term PRISEs (progesterone 5β-reductase/iridoid synthase-like enzymes) as it was previously demonstrated that they are involved in cardenolide and/or iridoid biosynthesis in other plants. In order to further understand the general role of PRISEs and to detect additional more “accidental” roles we herein characterized A. thaliana steroid reductase 1 (AtStR1) and compared it to A. thaliana steroid reductase 2 (AtStR2). We used A. thaliana Col-0 wildtype plants as well as VEP1 knockout mutants and VEP1 knockout mutants overexpressing either AtStR1 or AtStR2 to investigate the effects on vein patterning and on the stress response after treatment with methyl vinyl ketone (MVK). Our results added evidence to the assumption that AtStR1 and AtStR2, as well as PRISEs in general, play specific roles in stress and defense situations and may be responsible for sudden metabolic shifts.

Genetic optimization for engine combustion system calibration: A case study of optimization performance sensitivity to algorithm search parameters

The use of genetic optimization algorithms (GOA) has been shown to significantly reduce the resource intensity of engine calibration, motivating investigation into the development of these methods. The objective of this work was to quantify the sensitivity of GOA performance to the algorithm search parameter values, in a case study of engine calibration. A GOA was used to calibrate four combustion system control parameters for a direct-injection gasoline engine at a single operating condition, with an optimization goal to minimize brake specific fuel consumption (BSFC) for a specified engine-out NOx concentration limit. The calibration process was repeated for two NOx limit values and a wide range of values for five GOA search parameters, including the number of genes, mutation rate, and convergence criteria. Results indicated GOA performance is very sensitive to algorithm search parameter values, with converged calibrations yielding BSFC values from 1 to 14% higher than the global minimum value, and the number of iterations required to converge ranging from 10 to 3,000. Broadly, GOA performance sensitivity was found to increase as the NOx limit was decreased from 4,500 to 1,000 ppm. GOA performance was the most sensitive to the number of genes and the gene mutation rate, whereas sensitivity to convergence criteria values was minimal. Identification of one set of algorithm search parameter values which universally maximized GOA performance was not possible as ideal values depended strongly on engine behavior, NOx limit, and the maximum level of error acceptable to the user.

Genome sequence of Corynebacterium Amycolatum ICIS 99 Isolated From Human Vagina Reveals Safety and Beneficial Properties

Corynebacterium amycolatum ICIS 99 was isolated from vaginal smears of healthy women and showed promising results in antimicrobial screenings. Here, we report the draft genome sequence of this strain and analyze its main features to assess its safety and useful properties. The genome is 2,532,503 bp long and contains 2,186 CDSs with an average G+C content of 59.0%. Analyses of the ICIS 99 genome revealed the absence of true virulence genes. The genome contains genes involved in the synthesis of secondary metabolites and bacteriocins of the class sactipeptide. In the genome of ICIS 99, we identified a large number of genes responsible for adaptation and survival in the vaginal environment, including acid and oxidative stress resistance genes. The genomic information of ICIS 99 provides a basis for understanding the safety and useful properties of ICIS 99 and for considering it as a potential probiotic strain. However, further research is needed to test its probiotic efficacy in vivo. The whole genome shotgun project has been deposited at DDBJ/ENA/GenBank under the accession number JAIUSU000000000.

Gene by environment interaction mouse model reveals a functional role for 5-hydroxymethylcytosine in neurodevelopmental disorders

Mouse knockouts of Cntnap2 exhibit altered neurodevelopmental behavior, deficits in striatal GABAergic signaling and a genome-wide disruption of an environmentally sensitive DNA methylation modification (5-hydroxymethylcytosine, 5hmC) in the orthologs of a significant number of genes implicated in human neurodevelopmental disorders. We tested adult Cntnap2 heterozygous mice (Cntnap2+/-, lacking behavioral or neuropathological abnormalities) subjected to a prenatal stress and found that prenatally stressed Cntnap2+/- female mice showed repetitive behaviors and altered sociability, similar to the homozygote phenotype. Genomic profiling revealed disruptions in hippocampal and striatal 5hmC levels that were correlated to altered transcript levels of genes linked to these phenotypes (e.g., Reln, Dst, Trio, and Epha5). Chromatin-immunoprecipitation coupled with high-throughput sequencing and hippocampal nuclear lysate pull-down data indicated that 5hmC abundance alters the binding of the transcription factor CLOCK in the promoters of these genes (e.g., Palld, Gigyf1, and Fry), providing a mechanistic role for 5hmC in gene regulation. Together, these data support gene by environment hypotheses for the origins of mental illness and provide a means to identify the elusive factors contributing to complex human diseases.