A Critical Review of Gene Marker Selection Methods and Cell Count Inference Tools

Seasonal influenza virus is a threat for human being. Understanding dynamical change of immune response induced by influenza infection could benefit diagnosis and drug development, using transcriptome analysis. But transcriptomic data is often complicated by the changing cell makeup of the tissue during disease. It’s difficult to distinguish between gene regulations and cell proliferation or migration. Therefore inference of the change in cell counts is necessary, and computational models for cell count inference are introduced in this thesis. Besides, in most models related to prediction of cell quantities, gene marker selection is used as the first step. Thus computational methodology concerning gene marker selection for cell count inference is also reviewed. Different gene marker selection methods are applied to a common dataset to evaluate their behaviors. The uniqueness and expression intensity are the key properties for evaluation of obtained markers. As for predicting cell enrichment, principles of three kinds of schemes are explained. Computational algorithms named CTen and CIBERSORT are introduced as examples of them. Estimation behaviors of these tools are tested by a microarray dataset. Analysis of the estimations shows that they may provide good estimation but are not suitable for careful study of complex problems, e.g. dynamical samples

Big Data Approaches to Improving the Identification of Drug or Disease Mechanisms for Drug Innovation

Advances in science and technology have substantially changed drug research and development (R&D) processes. However, the efficiency of drug R&D, described in the number of new drugs approved per billion US dollars spent, dramatically declined between 1950 to 2010. Some of the main causes to the attrition include the cautious regulator, the potential risk in chemical screening methods for early drug discovery, and the lack of understanding of disease mechanisms. In order to improve the efficiency and productivity in drug R&D, more powerful tools are needed to assist in prediction forecasting and decision making in drug development. This dissertation describes my work in developing computational approaches to provide better understanding of drug or disease mechanisms at the systems level. The first project involves collaboration with RIKEN institute in Japan for innovation of influenza vaccine adjuvant. We performed comparative analysis of RNA-Seq data from mice treated with different adjuvants to identify mechanisms supporting adjuvant activity. In the second project, we predicted immune cell dynamics by linear regression-based algorithms or statistical tools, and suggested a new approach that can improve the discovery of key disease-associated genes. In the third project, we found that the network topological features, especially network betweenness, predominantly define the accuracy of a major drug target inference algorithm. We proposed a novel algorithm, TREAP, which integrated betweenness and differential gene expression and can accurately predict drug targets in a time-efficient manner. Through the projects above, we have demonstrated how computational algorithms can assist in mining big biological data to improve understanding of drug or disease mechanisms for drug innovation and development

Comprehensively Surveying Structure and Function of RING Domains from Drosophila melanogaster

Using a complete set of RING domains from Drosophila melanogaster, all the solved RING domains and cocrystal structures of RING-containing ubiquitin-ligases (RING-E3) and ubiquitin-conjugating enzyme (E2) pairs, we analyzed RING domains structures from their primary to quarternary structures. The results showed that: i) putative orthologs of RING domains between Drosophila melanogaster and the human largely occur (118/139, 84.9%); ii) of the 118 orthologous pairs from Drosophila melanogaster and the human, 117 pairs (117/118, 99.2%) were found to retain entirely uniform domain architectures, only Iap2/Diap2 experienced evolutionary expansion of domain architecture; iii) 4 evolutionary structurally conserved regions (SCRs) are responsible for homologous folding of RING domains at the superfamily level; iv) besides the conserved Cys/His chelating zinc ions, 6 equivalent residues (4 hydrophobic and 2 polar residues) in the SCRs possess good-consensus and conservation- these 4 SCRs function in the structural positioning of 6 equivalent residues as determinants for RING-E3 catalysis; v) members of these RING proteins located nucleus, multiple subcellular compartments, membrane protein and mitochondrion are respectively 42 (42/139, 30.2%), 71 (71/139, 51.1%), 22 (22/139, 15.8%) and 4 (4/139, 2.9%); vi) CG15104 (Topors) and CG1134 (Mul1) in C3HC4, and CG3929 (Deltex) in C3H2C3 seem to display broader E2s binding profiles than other RING-E3s; vii) analyzing intermolecular interfaces of E2/RING-E3 complexes indicate that residues directly interacting with E2s are all from the SCRs in RING domains. Of the 6 residues, 2 hydrophobic ones contribute to constructing the conserved hydrophobic core, while the 2 hydrophobic and 2 polar residues directly participate in E2/RING-E3 interactions. Based on sequence and structural data, SCRs, conserved equivalent residues and features of intermolecular interfaces were extracted, highlighting the presence of a nucleus for RING domain fold and formation of catalytic core in which related residues and regions exhibit preferential evolutionary conservation

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

Predicting Host Immune Cell Dynamics and Key Disease-Associated Genes Using Tissue Transcriptional Profiles

Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests

Biocontrol of Penicillium digitatum on Postharvest Citrus Fruits by Pseudomonas fluorescens

The effectiveness of the bacteria antagonist Pseudomonas fluorescens to control green mold caused by Penicillium digitatum on oranges (Citrus sinensis Osbeck, cv. Jincheng) and the possible modes of action were evaluated. Whether in vitro or in vivo, treatments with cell-free autoclaved cultures or culture filtrate had limited capacity to suppress P. digitatum, while P. digitatum was significantly inhibited by bacterial fluid (P. fluorescens in the nutrient broth liquid medium) and bacterial suspension (P. fluorescens in sterile distilled water) with living cells. There was a positive relationship between the concentration of P. fluorescens in bacterial suspension and its biological efficacy. In addition, P. fluorescens was effective when applied preventatively but not when applied curatively. In the inoculated wounds, the population of P. fluorescens was an approximately 28- and 34-fold increase after being incubated at 20°C for 8 d and at 4°C for 16 d, respectively, and P. digitatum could effectively stimulate the growth and reproduction of P. fluorescens. Moreover, P. fluorescens was able to inhibit spore germination and germ tube elongation of P. digitatum as well as induce resistance on citrus peel by increasing the chitinase (CHI) activity and advancing the activities peaks of β-1,3-glucanase (GLU), peroxidase (POD), and phenylalanine ammonia lyase (PAL). All of these results support the potential application of P. fluorescens against green mold on postharvest citrus

The cross-sectional study of hepatic lipase SNPs and plasma lipid levels

By the combination of meta-analysis, the data of the 1,000 Genomes Project Phase 3, and the promoter sequence of hepatic lipase (LIPC), we performed the cross-sectional study to explore the associations of four variants (rs1077835; rs1077834; rs1800588 [C-514T], and rs2070895 [G-250A]) in LIPC promoter with plasma lipid levels. Our results indicate that the first and the next three of the four SNPs are, respectively, reported to be associated with the decreased and increased HDL-c level. Meta-analysis of 87 studies with 101,988 participants indicates that HDL-c level in rs1800588 (C-514T) (pooled mean difference = 0.03, 95%CI (0.03, 0.04), p < .001) and rs2070895 (G-250A) (pooled mean difference = 0.07, 95%CI (0.05, 0.09), p < .001) is higher in allele T or A carriers. Similarly, LDL-c, TC, TG, and BMI levels are generally increased in T or A alleles carriers. We failed to conduct the meta-analysis of rs1077835 and rs1077834 due to the limited previous reports. Data from the 1,000 Genomes indicate that the allele frequencies of the four SNPs in total or subpopulations are almost equal to each other. The paired value r2 and D' of the four SNPs are larger than 0.8, which indicate the linkage disequilibrium of the four variants. The analysis of LIPC promoter indicate that C-514T and G-250A are, respectively, located in transcriptional factor binding sites of USF1and Pbx1b, which may partly explain the effect of the two SNPs on the decreased LIPC activity in the alleles carriers and the corresponding increased plasma lipids hydrolyzed by LIPC. These results may help us to better understand the different effects of the four SNPs on the plasma lipid levels among subpopulations and offer clues for future clinical treatment of dyslipidemia-related diseases

Single-cell analysis reveals individual spore responses to simulated space vacuum

Outer space is a challenging environment for all forms of life, and dormant spores of bacteria have been frequently used to study the survival of terrestrial life in a space journey. Previous work showed that outer space vacuum alone can kill bacterial spores. However, the responses and mechanisms of resistance of individual spores to space vacuum are unclear. Here, we examined spores" molecular changes under simulated space vacuum (~10−5 Pa) using micro-Raman spectroscopy and found that this vacuum did not cause significant denaturation of spore protein. Then, live-cell microscopy was developed to investigate the temporal events during germination, outgrowth, and growth of individual Bacillus spores. The results showed that after exposure to simulated space vacuum for 10 days, viability of spores of two Bacillus species was reduced up to 35%, but all spores retained their large Ca2 +-dipicolinic acid depot. Some of the killed spores did not germinate, and the remaining germinated but did not proceed to vegetative growth. The vacuum treatment slowed spore germination, and changed average times of all major germination events. In addition, viable vacuum-treated spores exhibited much greater sensitivity than untreated spores to dry heat and hyperosmotic stress. Among spores" resistance mechanisms to high vacuum, DNA-protective a/β−type small acid-soluble proteins, and non- homologous end joining and base excision repair of DNA played the most important roles, especially against multiple cycles of vacuum treatment. Overall, these results give new insight into individual spore"s responses to space vacuum and provide new techniques for microorganism analysis at the single-cell level

Enhanced Electrochemical Performance of LiNi_0.5Mn_1.5O₄ Cathode Material by YPO₄ Surface Modification

Cathode material LiNi_0.5Mn_1.5O₄ (LNMO) for lithium-ion batteries is successfully synthesized by a sol–gel method and is further modified by a thin layer of YPO₄ (1, 3, and 5 wt %) through a simple wet chemical strategy. Physical characterizations indicate that the YPO₄ nanolayer has a little impact on the cathode structure. Electrochemical optimization reveals that the 3 wt % YPO₄-coated LNMO could still deliver a high specific capacity of 107 mAh g^–1 after 240 cycles, with a capacity retention of 77.5%, much higher than that of the pristine electrode. Electrochemical impedance spectroscopy (EIS) analysis proves that the rapid increase of surface impedance could be suppressed by the YPO₄ coating layer and thus facilitates the surface kinetics behavior in repeated cycling. Through further material aging experiments, the improvement of electrochemical performances could be attributed to the formation of Lewis acid YF₃, converted from the YPO₄ coating layer in the LiPF₆-based electrolyte, which not only scavenges the surface insulating alkaline species with a high acidity but also accelerates ion exchange on the material surface and thus helps to generate the solid solution Li–Ni–Mn–Y–O on the surface of YPO₄-coated LNMO