Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets.

Transcriptome represents the expression levels of many genes in a sample and has been widely used in biological research and clinical practice. Researchers usually focused on transcriptomic biomarkers with differential representations between a phenotype group and a control group of samples. This study presented a multitask graph-attention network (GAT) learning framework to learn the complex inter-genic interactions of the reference samples. A demonstrative reference model was pre-trained on the healthy samples (HealthModel), which could be directly used to generate the model-based quantitative transcriptional regulation (mqTrans) view of the independent test transcriptomes. The generated mqTrans view of transcriptomes was demonstrated by prediction tasks and dark biomarker detection. The coined term "dark biomarker" stemmed from its definition that a dark biomarker showed differential representation in the mqTrans view but no differential expression in its original expression level. A dark biomarker was always overlooked in traditional biomarker detection studies due to the absence of differential expression. The source code and the manual of the pipeline HealthModelPipe can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

First complete mitogenome of Massarineae and its contribution to phylogenetic implications in Pleosporales.

Endophytic fungi play an important role in the growth and development of traditional Chinese medicine plants. We isolated a strain of Acrocalymma vagum from the endophytic fungi of the traditional Chinese plants Paris. To accurately identify this endophytic fungal species of interest, we sequenced the mitochondrial genome of A. vagum, which is the first discovered mitochondrial genome in Massarineae. The A. vagum mitochondrial genome consists of a 35,079-bp closed circular DNA molecule containing 36 genes. Then, we compared the general sequence characteristics of A. vagum with those of Pleosporales, and the second structure of the 22 tRNAs was predicted. The phylogenetic relationship of A. vagum was constructed using two different data sets (protein-coding genes and amino acids). The phylogenetic tree shows that A. vagum is located at the root of Pleosporales. The analysis of introns shows that the number of introns increases with the increase in branch length. The results showed that monophyly was confirmed for all families in Pleosporales except for Pleosporaceae. A. vagum is an ancient species in the Pleosporales, and Pleosporaceae may require further revision. In Pleosporales, the number of introns is positively correlated with branch length, providing data for further study on the origin of introns.

Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a rapidly developing and sometimes lethal pulmonary disease. Accurately predicting COVID-19 mortality will facilitate optimal patient treatment and medical resource deployment, but the clinical practice still needs to address it. Both complete blood counts and cytokine levels were observed to be modified by COVID-19 infection. This study aimed to use inexpensive and easily accessible complete blood counts to build an accurate COVID-19 mortality prediction model. The cytokine fluctuations reflect the inflammatory storm induced by COVID-19, but their levels are not as commonly accessible as complete blood counts. Therefore, this study explored the possibility of predicting cytokine levels based on complete blood counts. METHODS: We used complete blood counts to predict cytokine levels. The predictive model includes an autoencoder, principal component analysis, and linear regression models. We used classifiers such as support vector machine and feature selection models such as adaptive boost to predict the mortality of COVID-19 patients. RESULTS: Complete blood counts and original cytokine levels reached the COVID-19 mortality classification area under the curve (AUC) values of 0.9678 and 0.9111, respectively, and the cytokine levels predicted by the feature set alone reached the classification AUC value of 0.9844. The predicted cytokine levels were more significantly associated with COVID-19 mortality than the original values. CONCLUSIONS: Integrating the predicted cytokine levels and complete blood counts improved a COVID-19 mortality prediction model using complete blood counts only. Both the cytokine level prediction models and the COVID-19 mortality prediction models are publicly available at http://www.healthinformaticslab.org/supp/resources.php .

Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer.

This work hypothesizes that some genes undergo radically changed transcription regulations (TRs) in breast cancer (BC), but don't show differential expressions for unknown reasons. The TR of a gene is quantitatively formulated by a regression model between the expression of this gene and multiple transcription factors (TFs). The difference between the predicted and real expression levels of a gene in a query sample is defined as the mqTrans value of this gene, which quantitatively reflects its regulatory changes. This work systematically screens the undifferentially expressed genes with differentially expressed mqTrans values in 1036 samples across five datasets and three ethnic groups. This study calls the 25 genes satisfying the above hypothesis in at least four datasets as dark biomarkers, and the strong dark biomarker gene CXXC5 (CXXC Finger Protein 5) is even supported by all the five independent BC datasets. Although CXXC5 does not show differential expressions in BC, its transcription regulations show quantitative associations with BCs in diversified cohorts. The overlapping long noncoding RNAs (lncRNAs) may have contributed their transcripts to the expression miscalculations of dark biomarkers. The mqTrans analysis serves as a complementary view of the transcriptome-based detections of biomarkers that are ignored by many existing studies.

Orchestrating information across tissues via a novel multitask GAT framework to improve quantitative gene regulation relation modeling for survival analysis.

Survival analysis is critical to cancer prognosis estimation. High-throughput technologies facilitate the increase in the dimension of genic features, but the number of clinical samples in cohorts is relatively small due to various reasons, including difficulties in participant recruitment and high data-generation costs. Transcriptome is one of the most abundantly available OMIC (referring to the high-throughput data, including genomic, transcriptomic, proteomic and epigenomic) data types. This study introduced a multitask graph attention network (GAT) framework DQSurv for the survival analysis task. We first used a large dataset of healthy tissue samples to pretrain the GAT-based HealthModel for the quantitative measurement of the gene regulatory relations. The multitask survival analysis framework DQSurv used the idea of transfer learning to initiate the GAT model with the pretrained HealthModel and further fine-tuned this model using two tasks i.e. the main task of survival analysis and the auxiliary task of gene expression prediction. This refined GAT was denoted as DiseaseModel. We fused the original transcriptomic features with the difference vector between the latent features encoded by the HealthModel and DiseaseModel for the final task of survival analysis. The proposed DQSurv model stably outperformed the existing models for the survival analysis of 10 benchmark cancer types and an independent dataset. The ablation study also supported the necessity of the main modules. We released the codes and the pretrained HealthModel to facilitate the feature encodings and survival analysis of transcriptome-based future studies, especially on small datasets. The model and the code are available at http://www.healthinformaticslab.org/supp/.

Transcriptional Dysregulations of Seven Non-Differentially Expressed Genes as Biomarkers of Metastatic Colon Cancer.

Background: Colon cancer (CC) is common, and the mortality rate greatly increases as the disease progresses to the metastatic stage. Early detection of metastatic colon cancer (mCC) is crucial for reducing the mortality rate. Most previous studies have focused on the top-ranked differentially expressed transcriptomic biomarkers between mCC and primary CC while ignoring non-differentially expressed genes. Results: This study proposed that the complicated inter-feature correlations could be quantitatively formulated as a complementary transcriptomic view. We used a regression model to formulate the correlation between the expression levels of a messenger RNA (mRNA) and its regulatory transcription factors (TFs). The change between the predicted and real expression levels of a query mRNA was defined as the mqTrans value in the given sample, reflecting transcription regulatory changes compared with the model-training samples. A dark biomarker in mCC is defined as an mRNA gene that is non-differentially expressed in mCC but demonstrates mqTrans values significantly associated with mCC. This study detected seven dark biomarkers using 805 samples from three independent datasets. Evidence from the literature supports the role of some of these dark biomarkers. Conclusions: This study presented a complementary high-dimensional analysis procedure for transcriptome-based biomarker investigations with a case study on mCC.

Gender-specific dysregulations of nondifferentially expressed biomarkers of metastatic colon cancer.

Colon cancer is a common cancer type in both sexes and its mortality rate increases at the metastatic stage. Most studies exclude nondifferentially expressed genes from biomarker analysis of metastatic colon cancers. The motivation of this study is to find the latent associations of the nondifferentially expressed genes with metastatic colon cancers and to evaluate the gender specificity of such associations. This study formulates the expression level prediction of a gene as a regression model trained for primary colon cancers. The difference between a gene's predicted and original expression levels in a testing sample is defined as its mqTrans value (model-based quantitative measure of transcription regulation), which quantitatively measures the change of the gene's transcription regulation in this testing sample. We use the mqTrans analysis to detect the messenger RNA (mRNA) genes with nondifferential expression on their original expression levels but differentially expressed mqTrans values between primary and metastatic colon cancers. These genes are referred to as dark biomarkers of metastatic colon cancer. All dark biomarker genes were verified by two transcriptome profiling technologies, RNA-seq and microarray. The mqTrans analysis of a mixed cohort of both sexes could not recover gender-specific dark biomarkers. Most dark biomarkers overlap with long non-coding RNAs (lncRNAs), and these lncRNAs might have contributed their transcripts to calculating the dark biomarkers' expression levels. Therefore, mqTrans analysis serves as a complementary approach to identify dark biomarkers generally ignored by conventional studies, and it is essential to separate the female and male samples into two analysis experiments. The dataset and mqTrans analysis code are available at https://figshare.com/articles/dataset/22250536.

Sensitive Determination of Trace 4-Nitrophenol in Ambient Environment Using a Glassy Carbon Electrode Modified with Formamide-Converted Nitrogen-Doped Carbon Materials.

Sensing trace amounts of 4-nitrophenol (4-NP) as a harmful substance to organisms even in small quantities is of great importance. The present study includes a sensitive and selective electrochemical sensor for detecting 4-NP in natural water samples using formamide-converted nitrogen-carbon materials (shortened to f-NC) as a new material for electrode modification. The structure and morphology of the f-NC were set apart by SEM, TEM, XRD, XPS, FTIR, Raman, and the electrochemical performance of the f-NC were set apart by CV, EIS and CC. We studied the electrochemical behaviour of 4-NP on the glassy carbon electrode modified with f-NC before and after pyrolysis treatment (denoted as f-NC1/GCE and f-NC2/GCE). In 0.2 M of H2SO4 solution, the f-NC2/GCE has an apparent electrocatalytic activity to reduce 4-NP. Under the optimal conditions, the reduction peak current of 4-NP varies linearly, with its concentration in the range of 0.2 to 100 mM, and the detection limit obtained as 0.02 mM (S/N = 3). In addition, the electrochemical sensor has high selectivity, and the stability is quite good. The preparation and application of the sensor to detect 4-NP in water samples produced satisfactory results, which provides a new method for the simple, sensitive and quantitative detection of 4-NP.

Nickel/nitrogen-doped carbon nanocomposites: Synthesis and electrochemical sensor for determination of p-nitrophenol in local environment.

A novel electrochemical sensor was prepared using N-doped carbon mesoporous materials supported with nickel nanoparticles (Ni-NCs) for environmental p-nitrophenol (p-NP) detection in a specific geographical area. These as-prepared Ni-NCs were dispersed in polyethyleneimine (PEI) solution and modified onto a glassy carbon electrode (GCE) for electrocatalytic reduction of p-NP. The Ni-NCs-PEI/GCE showed a high Faraday current at -0.302 V during p-NP reduction, because of the synergistic effect between Ni-NCs and PEI. Under ideal conditions, the Ni-NCs-PEI/GCE was used in the voltametric determination of p-NP, with high sensitivity. The linear ranges for p-NP are 0.06-10 µM and 10-100 µM with low detection limit (4.0 nM) and high sensitivity (1.465 µA µM-1 cm-2). In the presence of other phenolic compounds, this sensor showed good selectivity for p-NP detection. The Ni-NCs-PEI/GCE was also used to determine p-NP in environmental water samples of a specific geographical area, with recoveries ranging from 95.9% to 109.4%, and an RSD of less than 3.6%. Therefore, this novel Ni-NCs-PEI/GCE provides a good example for the design of other carbon-based nanocomposite materials, for electrochemical detection of trace p-NP in a specific geographical area.

A Potential Biofertilizer-Siderophilic Bacteria Isolated From the Rhizosphere of Paris polyphylla var. yunnanensis.

The increasing demands for crop production have become a great challenge while people also realizing the significance of reductions in synthetic chemical fertilizer use. Plant growth-promoting rhizobacteria (PGPR) are proven biofertilizers for increasing crop yields by promoting plant growth via various direct or indirect mechanisms. Siderophilic bacteria, as an important type of PGPR, can secrete siderophores to chelate unusable Fe3+ in the soil for plant growth. Siderophilic bacteria have been shown to play vital roles in preventing diseases and enhancing the growth of plants. Paris polyphylla var. yunnanensis (PPVY) is an important traditional Chinese herb. However, reports about its siderophilic bacteria are still rare. This study firstly isolated siderophilic bacteria from the rhizosphere soil of PPVY, identified by morphological and physio-biochemical characteristics as well as 16S rRNA sequence analysis. The dominant genus in the rhizobacteria of PPVY was Bacillus. Among 22 isolates, 21 isolates produced siderophores. The relative amount of siderophores ranged from 4 to 41%. Most of the isolates produced hydroxamate siderophores and some produced catechol. Four isolates belonging to Enterobacter produced the catechol type, and none of them produced carboxylate siderophores. Intriguingly, 16 strains could produce substances that have inhibitory activity against Candida albicans only in an iron-limited medium (SA medium). The effects of different concentrations of Fe3+ and three types of synthetic chemical fertilizers on AS19 growth, siderophore production, and swimming motility were first evaluated from multiple aspects. The study also found that the cell-free supernatant (CFS) with high siderophore units (SUs) of AS19 strain could significantly promote the germination of pepper and maize seeds and the development of the shoots and leaves of Gynura divaricata (Linn.). The bacterial solution of AS19 strain could significantly promote the elongation of the roots of G. divaricata (Linn.). Due to its combined traits promoting plant growth and seed germination, the AS19 has the potential to become a bioinoculant. This study will broaden the application prospects of the siderophilic bacteria-AS19 as biofertilizers for future sustainable agriculture.