extfieldsofstudy:
Computer Science
Medicine
Biology

text:
LnCompare: gene set feature analysis for human long non-coding RNAs

Abstract Interest in the biological roles of long noncoding RNAs (lncRNAs) has resulted in growing numbers of studies that produce large sets of candidate genes, for example, differentially expressed between two conditions. For sets of protein-coding genes, ontology and pathway analyses are powerful tools for generating new insights from statistical enrichment of gene features. Here we present the LnCompare web server, an equivalent resource for studying the properties of lncRNA gene sets. The Gene Set Feature Comparison mode tests for enrichment amongst a panel of quantitative and categorical features, spanning gene structure, evolutionary conservation, expression, subcellular localization, repetitive sequences and disease association. Moreover, in Similar Gene Identification mode, users may identify other lncRNAs by similarity across a defined range of features. Comprehensive results may be downloaded in tabular and graphical formats, in addition to the entire feature resource. LnCompare will empower researchers to extract useful hypotheses and candidates from lncRNA gene sets.

INTRODUCTION
Long non-coding RNAs (lncRNAs) are a numerous yet poorly understood class of genes with growing biological and biomedical interest. Their regulatory roles (1) and tissue specificity (2,3) make them promising biomarkers and therapeutic targets (4,5). High-throughput studies on disease or biological systems routinely produce sets of tens to thousands of lncRNA candidates (6). Some examples of such sets are lncRNAs exhibiting differential expression be-tween conditions (7), association with a disease (8) or whose perturbation by CRISPR-Cas9 leads to phenotypic changes (6,9). Important bottlenecks arise in assessing the quality of such sets, and generating functional and mechanistic hypotheses from them (6,7,10,11).
Controlled ontologies describing gene functions or their products' characteristics, most notably Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (12)(13)(14), are powerful and widely used tools for inspecting sets of protein-coding genes (PCGs) (15)(16)(17)(18). Unfortunately, functional labels have not yet been directly assigned to lncRNAs, making them inaccessible to ontology analyses. As a result, lncRNA candidate sets cannot be mined for biological insights to the degree which we have come to expect for PCGs. In a similar way, there are a range of tools designed to identify other PCGs with various degrees of similarity to a gene of interest (19)(20)(21)(22), but none are available for lncRNAs. In summary, there is a need for tools to analyse lncRNA gene lists and functionally prioritize candidates for further study.
To date, most methods dedicated to revealing functional insights from lncRNA sets rely on the biological properties of PCGs with correlated expression across tissues (5). For example, LncRNA2Function and Co-LncRNA web servers perform functional enrichment analysis on the co-expressed coding genes of the input lncRNAs (23,24). Lnc-GFP and LncRNAs2Pathways (23,25) follow a similar strategy but introducing more sophisticated graph theory algorithms on co-expression networks. Finally, FARNA (26) considers transcription factor (TF)-lncRNA associations to predict lncRNA functions. However, none of these resources directly explores the features of lncRNA genes and products.
To address this need, we have developed LnCompare, a web server that compares lncRNA genes across a range of features. LnCompare is based on a comprehensive feature set with more than 100 attributes covering diverse aspects, including gene structure, nucleotide composition, evolutionary conservation, cell and tissue expression, subcellular localization, tissue specificity, repetitive sequence content and phenotypic association. Based on these features, LnCompare has two main functionalities. First, Gene Set Feature Comparison identifies statistically-enriched features of lncRNA sets, in a similar way as is presently done for PCGs (17,18,27,28). Second, Similar Gene Discovery functionality seeks to identify other similar genes for a given gene-of-interest, based on user-defined features. LnCompare is freely available at http://www.rnanut.net/ lncompare/

Compilation of lncRNA features
We collected and processed various lncRNA datasets from public databases and in-house computational analysis. Altogether these comprise 109 gene/transcript attributes for the GENCODE v24 human lncRNAs annotation (15 941 genes). These features can be classified into six main classes (see Table 1): All data has been compiled at the level of lncRNA genes, not transcripts. For certain features, we utilized an exonic projection of all annotated transcripts from each gene, and estimated the corresponding feature accordingly--for example, GC content or phastCons overlap. Detailed information on every feature and its source are available in Supplementary Table S1, and provided as additional information at the web server. A comprehensive list of features may be found in Supplementary Table S1, and the entire table of lncRNA features for the GENCODE v24 annotation can be downloaded in the 'Download' tab in LnCompare.

Statistical analysis
In Gene Set Feature Comparison mode, LnCompare considers both quantitative and categorical features ( Figure 1A). By default, the background set is defined as the entire GEN-CODE annotation. The Wilcoxon test is used to compare quantitative features between the input gene set and the background set ( Figure 1B). For categorical features, the hypergeometric test is applied, and the detailed formula was described in our previous work (29) ( Figure 1C). By default, the quantitative features are sorted by the absolute logarithm ratio of the average feature values between the input versus background, in order to highlight the features where the input lncRNA list and the background show the most prominent divergence. Similarly, the odds ratio is used to rank most enriched categorical features.
In Similar Gene Discovery, LnCompare performs similarity calculation between two lncRNAs based on their features ( Figure 2). After comparing several methods (e.g. Pearson correlation, Euclidian distance etc.), the cosine similarity was finally adopted: where A and B are the feature vectors of two LncRNAs, with N/A values dropped. By definition, it is the point multiplication of A and B, divided by the product of the norm of the two vectors. To enable a flexible similarity calculation, users can either use all features, or else a defined subset of features (Supplementary Table S1). Higher cosine similarity indicates greater similarity. In addition, for more robust results, we employed mutual rank, which has been successfully applied to establish gene co-expression networks (30): Where the Rank(Similarity) A in B denotes the rank of similarity between A and B among the similarities of B to all other lncRNAs, and Rank(Similarity) B in A is defined in similar fashion. Lower mutual rank values indicate greater similarity.

Server implementation
The web server is built on a Linux server using the Apache+MySQL+PHP framework. All graphical visual-

DESCRIPTION OF WEB SERVER
LnCompare web server performs gene set or single gene comparisons of lncRNAs based on diverse features. It is based on the GENCODE version 24 human annotation (31), and therefore only genes with Ensembl 'ENSG. . . ' identifiers belonging to this annotation are assessed. In cases where a supported non-GENCODE identifiers are provided, LnCompare will attempt to map it to GEN-CODE. Supported identifiers comprise Gene symbols, Ref-Seq IDs and Ensembl transcript IDs, whose mappings to GENCODE are based on the Ensembl ID mapping file. When successfully mapped, this gene will be included in subsequent analyses. When not successful, unrecognized IDs, including out-of-date Ensembl entries, are ignored in analyses. The number of successfully found IDs is reported in the results page. For all analyses, users can populate forms with three different sets of example data using buttons. These are: the 'Simple Example' list of six randomly-selected lncRNAs; the 'CLC' list of 122 cancer-related lncRNAs (32); the 'Cell Cycle Example' of 117 lncRNAs that are differentially expressed between G1S and G2M cell-cycle stages in HeLa cells (33).
LnCompare has two modules, described below. A complete tutorial for both modules can be found in the 'Help' tab.

Gene set feature comparison
Gene Set Feature Comparison module aims to identify features that characterize a user-provided gene set. This input gene set is compared to a defined background gene set, across each feature ( Figure 1A). By default, background is the entire GENCODE annotation, although the user can provide alternative background sets.
The feature-comparison analysis runs differently for quantitative and categorical features. For quantitative features, statistical significance is assessed by Wilcoxon test, while for categorical features the hypergeometric test is used ( Figure 1B and C). Results are displayed separately.
For quantitative features, LnCompare returns a summary plot with logarithm ratios of the mean feature values for the input and the background sets. For categorical features, equivalent plots display the odds ratio ( Figure 1B and C). In addition, corresponding P-values, Benjamini-Hochberg false discovery rates (FDR) (34) and a link to a boxplot (or barplot for categorical features) can be found in tabular format (for an example see Figure 3). Additional information on each feature can be accessed from the '?' button in the table. For both quantitative and categorical features, the user can apply several different cutoffs to the data displayed: the top ten features, ranked by mean ratio/odds ratio (for quantitative and categorical features, respectively), features with P < 0.05, features with FDR < 0.05, or all possible comparisons). Graphical and tabular results can be directly downloaded from the website.

Similar gene discovery
LnCompare offers two approaches to compare similarity of lncRNA genes using cosine similarity method: (i) 1-to-N comparison: computes the similarity of one user-provided lncRNA to all remaining GENCODE lncRNAs; (ii) M-to-N comparison: computes the similarity of every lncRNA from list M to every one in list N (Figure 2). The user must provide two lists of Ensembl gene IDs (up to a maximum of 100 in each). After specifying the type of analysis desired (1-to-N or M-to-N) and entering the gene IDs, the user can choose which subsets of lncRNA features to be used for similarity analysis (from the feature classes described above (see 'Materials and Methods' section).
For both types of similarity analysis, graphical and tabular outputs display the top 10, 20 or 50 cosine scores (specified by the user) together with the corresponding gene IDs. The table also contains the relative rank the score represents among the partners for lncRNA1 and lncRNA2 lists, respectively (i.e. first number indicates the ranking number for that pair among all possible partners of lncRNA from lncRNA1 list, and the second number indicates the same for lncRNA from lncRNA2 list).
When all the feature classes are selected, LnCompare also provides a mutual rank similarity results section. In this case, graphical and tabular formats show how reciprocal the similarity is between the two genes, with a mutual rank score (see 'Materials and Methods' section for more details). Again, the user can select how many output comparisons should be displayed (top 10, 20 or 50 scores). All the tables from this module are available for download.

EVALUATION OF WEB SERVER; A CASE STUDY US-ING CANCER-RELATED GENES
We tested the performance of LnCompare using a set of 122 experimentally validated cancer lncRNAs from the Cancer LncRNA Census (CLC) (32). CLC genes are curated based on experimentally validated functional roles in tumorigenesis or cancer-related cellular phenotypes, and hence is a useful positive control set of lncRNA genes. The CLC genes make a good test case, since they are known to be characterized by a range of features such as high expression in tumours, spliced length and evolutionary conservation (32). Assessing CLC we want to represent two possible scenarios: (i) the user has no prior knowledge of a gene set, and wishes to assess their potential functionality; (ii) The user is aware that this is a set of important lncRNAs, and wishes to study their particular features.
Running the Gene Set Feature Comparison module, we searched for specific features of CLC genes compared to background (all other lncRNAs). Using a cutoff of FDR < 0.05, we observe several quantitative and qualitative traits to be significantly enriched in CLC lncRNAs ( Figure 3A). These include high average expression across numerous human cell lines and tissues. CLC genes, on average, also show high exon and promoter conservation across mammals and vertebrates ( Figure 3A). Moreover, the CLC set is significantly enriched with lncRNAs from functional and disease databases ( Figure 3B). Together, these attributes are consistent with the input gene set being enriched with bona fide functional lncRNAs (32), and points to features (e.g. high expression) that are shared by both cancer-related lncRNAs and PCGs (35,36).
Interestingly, the Gene Set Feature Comparison also reports CLC genes to be on average closer in genomic distance to PCGs, and significantly more likely to be divergently transcribed from protein coding genes ( Figure 3B). This may reflect a common molecular mechanism among CLC lncRNAs to be further studied. In contrast, it may result from a bias in literature to focus on lncRNAs that lie close to PCGs. Such ascertainment biases are an important confounding factor that should be borne in mind when interpreting these results. At last, we also observe that the W528 Nucleic Acids Research, 2019, Vol. 47, Web Server issue CLC set tends to be less tissue specific, more cytoplasmic (their nuclear/cytoplasmic ratio is significantly lower) (Figure 3A), and enriched with hits from proliferation CRISPR screens ( Figure 3B).
Similar Gene Discovery 'M-to-N' functionality may complement the above analysis to interrogate unknown lists of genes and help to prioritize candidates based on similarity to known lncRNAs. For example, to select potential cancerassociated lncRNAs for experimental validation from a list of novel candidates, one can assess their similarity to CLC genes.
In addition, Similar Gene Discovery '1-to-N' functionality makes it possible to search for the most similar genes to a given lncRNA, in order to discover new, functionallyrelated lncRNAs. For example, in searching for lncR-NAs similar to the X-inactive specific transcript, XIST (37), LnCompare reports the maternally-expressed gene 8 (MEG8) to be the most similar (cosine similarity 0.9). Interestingly, both genes are associated with imprinting, are expressed during early development and have nuclearrestricted localization (38)(39)(40). Moreover, MEG8 has been reported to interact with chromatin-binding proteins and repressor complexes (41,42). An important caveat is the fact that some lncRNAs are present in the functional database lncRNAdb, and this will influence the similarity analysis results without necessarily representing biological similarity. In order to eliminate this possible confounder, we removed the phenotype-associated feature class and repeated the analysis. This analysis now identifies ENSG00000272872 as the most similar to XIST. This lncRNA has been linked to various cancers in the lncRNADisease v2.0 database (43), making it an interesting candidate to study.

DISCUSSION
In recent years there has been a dramatic acceleration in the volumes of lncRNA gene candidates emerging from genomic studies. However, we remain broadly ignorant about molecular mechanisms and biological roles of these genes. Classical methods to describe newly discovered genes or to assess gene sets are inefficient for lncRNAs. This has created a need for tools to study the properties of lncRNA sets, in order to formulate new hypotheses from or gauge the success of high-throughput experiments.
To meet this need, we have curated a comprehensive feature set covering diverge quantitative and categorical aspects of lncRNAs from the GENCODE annotation. Using these features, LnCompare searches for those that are significantly over-or under-represented in an input set compared to background. In a set of lncRNAs with known roles in cancer (35), LnCompare identifies a range of characteristic features, including elevated expression and evolutionary conservation. Moreover, it also identifies other attributes including higher cytoplasmic localization and ubiquitous expression. These features may guide researchers to focus on potential molecular activities related to cytoplasmic processes, in contrast to most studies that concentrate on lncRNAs' roles in chromatin regulation (44)(45)(46). Moreover, LnCompare assesses similarity between genes, which can be a powerful strategy to identify new genes playing similar roles to known examples. Conversely, such similarity analysis could be used to predict the roles of novel lncRNAs by similarity to known genes. We anticipate that LnCompare will be useful to the many colleagues who presently study lncRNAs at the global scale and wish to extract more biological insights from their data.

SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.


Electrostatic Read Out for Label‑Free Assays Based on Kelvin Force Principle

Detection methods and analytical devices have drawn increasing attention in recent years due to their direct impact on early detection, monitoring and diagnosis of dis-ease in medical research. In this work, we describe a simple but so far unrecognized label-free method for surface-bound analyte detection, which could be applicable to a wide range of substances. In this respect, the feasibility and practical aspects of a micrometer-scale, poly- l -lysine-based, solid-phase assay for label-free analyte detection with electrostatic read-out are investigated. Micropatterned poly- l -lysine layers were produced using soft-lithography on mica and their electrostatic surface potential was determined using Kelvin-probe Force Microscopy. Ribose, a natural sugar, was used as analyte. Upon exposure to ribose, the surface potential changed from positive to negative in a reversible manner. We report for the first time the use of an electrostatic principle for assay read-out. This purely physical effect could be used to develop label- and marker-free assays for sugars, various other substances or, possibly, biosensors. Graphical Abstract

Introduction
High-throughput and parallel detection of minimal amounts of biochemical substances can be achieved using the microarray format as in DNA chips or protein microarrays [1]. These are technically a form of microscopic solid-phase assay, that is, an analyte binding-partner is attached on a solid support and then binds to the desired analyte(s) [2]. Analyte-binding is then read out using a physical quantity such as an optical (e.g. fluorescence, colourimetry, transmittance, plasmon resonance, etc.) or a radiative signal (radio-immunoassay).
A drawback of these read-out methods is that they often require some form of labelling (fluorescence, radio-labelling, etc.) or the introduction of additional materials (e.g. marker particles), which could interfere with the actual analyte binding mechanism.
A label-free read-out alternative is the use of an electrostatic signal, that is, the detection of changes of the electrostatic surface potential of the solid support upon analyte-binding. This was demonstrated using the Kelvin-probe principle on the example of the well-established avidin-biotin reaction, complementary DNA-binding [3], aptamer-binding [4], or ligand-receptor binding [5]. In these assays, the analyte-binding mechanism is highly specific, as in the example of complementary DNA-binding [3]. However, situations are conceivable where a more generic mechanism could be useful. One such mechanism could be an ionic interaction of the analyte with its binding-partner.
In the present paper, we explore the combination of highly charged poly-l-lysine (PLL) as a generic analyte binding-partner with the Kelvin-probe method as the read-out (Fig. 1). The motivation and the advantage of PLL is that it readily adsorbs on typical, solid supports such as glass or mica in a defined and fairly homogenous layer, its properties are well-known, it is biocompatible (it is a component of many foods and is routinely used for enhancing cell adhesion), and it has a high charge due to the many lysines exposing the ionised ε-amine group. Especially the latter aspect is important for any electrostatic read-out as the Kelvin-probe principle, which essentially detects alterations of surface charge. An additional advantage of PLL is that its amine groups could serve as attack points for a variety of more specific, chemical reactions such as glycation, which typically starts with a Schiff-base formation between amine-groups of peptides and carbonyl groups of carbohydrates.
In order to investigate the basic properties and aspects of this concept, we employed Kelvin-probe Force Microscopy (KFM or KPFM) to detect alterations of the surface potential of the micropatterned PLL-structures (Fig. 1b, d).
In KFM, a conductive tip is held at very close distance (a few nm) of the surface, an AC+DC voltage is applied between tip and surface (Fig. 1b), and the resulting tip oscillation is recorded [6]. There are a number of variants, such as amplitude modulation vs frequency modulation, or open-loop vs closed-loop methods [7]. However, all have in common that the electrostatic properties of the surface (surface potential or surface charge) ultimately influence the tip oscillation and, hence, the recorded signal. As our objective was to produce test structures on the micrometer scale, we used well-established soft-lithography techniques based on siloxane rubber stamps for micropattering [8].

Patterning Methods
Elastomeric polydimethylsiloxane (PDMS) stamps were used to create PLL patterns. The stamps were produced using the procedure described in the literature [9] and the curing was performed by heating the polymer/curing agent mixture at 50 °C for 2 hours in a plastic petri dish on a hot plate. To deposit patterns of PLL the microfluidic-channel deposition method was used [10].
Microfluidic-channel deposition was performed by placing a fresh PDMS stamp (10 μm wide channels, 30 μm wide ridges) on the substrate (Fig. 1a). The channels were filled up by pipetting around 40 μL of poly-l-lysine solution (Sigma-Aldrich, CAS-#: 25988-63-0, 0.01% w/v in water) at the open ends of the channels. After 5 min, when the channels were visually filled up with PLL solution, deionised (DI) water was flushed into the channels and the stamp was removed. Finally, the sample was rinsed with DI-water and dried either with a gentle stream of nitrogen or a hand-held air bellows.

AFM/KFM
AFM/KFM was performed using a Dimension Icon PT AFM or a Multimode 8 AFM both with Nanoscope V controller (Bruker Corporation, Billerica MA, USA) in ambient air (relative humidity = 20-30%, temperature = 21-25 °C) (Fig. 1b, d). Conductive silicon tips (Veeco antimony (n) doped Si, NTESPA) were used with nominal spring constants of 40 N/m. The surface potential mode was used for KFM imaging. All images of a given data set were taken using the same imaging-parameters (lift height, controller gains, scan speeds, lines/image, etc.).

Image Analysis
All image data was analysed using the free, third-party data analysis software Gwyddion (gwyddion.net). AFM topography and KFM surface potential images were 1st-order or 2nd-order line-levelled excluding the features of interest (e.g. the PLL stripes in Fig. 2b) using the mask function. The average potential and RMS value of a designated area were determined using the statistical quantities function. All potential data represents the potential value of the area of interest minus the potential value of the immediately surrounding substrate. Figure 2 shows the results of patterning PLL layers on mica. The channels mechanically restricted the adsorption to stripes of 10 μm width with a 30 μm gap inbetween. The topography images (Fig. 2a as an example) show barely any features, which indicates that the PLL layers are very thin, whereas the potential images (Fig. 2b) show a clear, positive signal of approximately + 80 mV ( Fig. 2d) with respect to the surrounding mica, with the geometry expected from the patterning procedure.

Microfluidic-Channel Deposition
In order to be useable for subsequent target-binding and read-out by KFM, the PLL patterns need to be sufficiently stable, that is, their surface potential should not change significantly upon scanning or immersion in pure water, that is, without exposure to the actual analyte. It is conceivable that repeatedly scanning PLL patterns could alter their surface potential, for example through mechanical removal of PLL from the substrate or dissipation of surface charge through the conductive AFM tip during the scanning. Furthermore, although mica is a well-established and very suitable substrate for AFM-based experiments, the fact that highly mobile cations desorb from the mica surface when exposed to water and, possibly, migrate in the thin water layer present in ambient air, could lead to an unwanted alteration of the surface charge of mica, which would then introduce an unwanted artifact. Figure 2e shows the evolution of the PLL potential signal with repeated scans under the same conditions. It can be seen that, although the first 2-3 scans do not yet alter the signal significantly, it slowly decays to smaller values within 8 scans. This could be due to the aforementioned influences. Furthermore, scan number 9 was taken 48 h after scan number 8 and the drop in signal observed indicates that there is a substantial amount of charge decay on the sample surface even when it does not come into contact with the tip. This charge decay could be due to surface migration of charge carriers, possibly caused by a thin layer of water on the sample.
The latter effect would be particularly prominent whenever immersing the substrate in the assay solution. We, therefore, investigated if repeatedly immersing the patterned substrate in DI-water caused signal decay. Figure 2f shows that the signal varies by about ± 20% upon immersion (and subsequent drying) but no obvious trend could be discerned and, after the 10 th immersion, the signal still has approximately the same value as after the 1st immersion of slightly more than + 20 mV.

Application to Sugar Detection
As an assay-type application, we assessed the possibility of using PLL patterns for sugar detection. PLL patterns were produced as in Fig. 2 and were alternatingly immersed in DI-water for 1 min. and in 6-mM D-ribose solution for 1 min. After each immersion, the sample was dried by a gentle stream of air and then imaged by KFM. Figure 3 shows the results of the experiment. The potential images show a clear and well-defined PLL pattern, which exhibits a positive potential of +50 mV to +100 mV with respect to mica after immersion in DI-water (Fig. 3b) and a negative potential of about − 20 mV with respect to mica after immersion in ribose solution (Fig. 3d). Thus, a clear polarity reversal is observed upon exposure of the pattern to ribose. Figure 3e shows that the effect itself is reversible. Although there is a significant variation of the signal upon repeated immersions in water (blue data points in Fig. 3e), the signal drop upon immersion in the sugar solution in considerable greater and allows clear discrimination between pure water and sugar solution in a repeated manner.
The reversibility points to a physical adsorption process rather than to a (irreversible) chemical reaction between ribose and PLL. However, it is conceivable that, under different conditions, e.g. higher ribose concentration, higher temperature, longer incubation time, etc., a Maillard-or glycation-type reaction could occur as PLL offers many reactive amine groups at its surface.

Conclusions
In summary, we investigated practical aspects of using the Kelvin-probe principle as an electrostatic method to read out microstructured PLL assays and demonstrated the principle on the example of sugar detection. Regarding sensitivity, the serum fasting-level of ribose in human is 100 μM [11] Hence, the concentration of 6 mM we used here is still much higher. In contrast, reported values for experimental biosensors for glucose detection can range from 0.013 to 5.85 M [12] while others can reach as low as 300 nM as a detection limit [13]. Despite our system has not yet been tested under low concentrations of sugar, it is well known that KFM is capable of detecting changes in surface potential in the order of a few mV [14], which is a remarkable feature to be exploited in label-free detection technology. The detection limit, linearity and sensitivity would need to be further investigated.
However, our results show that it is possible to read out a basic sugar assay and they highlight that the baselines determined in Fig. 2e, f need to form an inherent part of any assay protocol. In the present paper, we did not investigate the effect of interferents on the read-out signal, which should be performed in future works. Interference is usually minimized by ensuring high specificity between the substrate and the analyte of interest. Here, we used PLL for practical reasons. However, the baseline could easily be replaced by using different molecules in the patterning-step, favouring specificity with the analyte. Furthermore, selective membranes to minimize interference could be used prior to performing the read out.
Obviously, KFM as implemented in commercial AFMs would not be suitable for routine read-out of microarrays in a typical, analytical laboratory setting. This is trivial insofar AFM/KFM is a technique developed for high, spatial resolution microscopy and not for signal detection. How could the Kelvin-probe principle and its high sensitivity be implemented efficiently with the purpose of assay read-out? Even in a microarray design, where the spot size is typically in the 10 to 100 μm range [15], it is not necessary to image every spot with nm-resolution. In fact, for assay read-out, it is not even necessary to record any image at all. The only information that is needed is the surface potential signal before and after the analyte binding-event. Hence, AFM scanner and tip are not needed. It is conceivable that Kelvinprobe-based read-out could be achieved, for example, with a multiplexed array of tipless cantilevers hovering in close proximity to the microarray spots on the substrate. Similar set-ups were developed in the early days of AFM [16] and could be adapted to perform electrostatic detection.


s2fieldsofstudy: Biology Medicine

text:
Long Noncoding RNA GATA3-AS1 Promotes Cell Proliferation and Metastasis in Hepatocellular Carcinoma by Suppression of PTEN, CDKN1A, and TP53

Background Long noncoding RNAs (lncRNAs) have been known to play important roles in the progression of various types of human cancer. LncRNA GATA3 antisense RNA 1, GATA3-AS1, has been reported to be associated with T-cell development and differentiation. However, the expression pattern and function of GATA3-AS1 in hepatocellular carcinoma (HCC) remain unknown. Methods Real-time quantitative PCR (RT-qPCR) assay was conducted to detect GATA3-AS1 expression levels in 80 cases of pairs HCC tissues and matched normal tissues. Chi-squared (χ2) test was used to analyze the correlation between GATA3-AS1 expression and clinicopathologic variables. Survival curves were plotted using the Kaplan–Meier method and were compared via the log-rank test. The cell counting kit-8 (CCK-8) and wound scratch assays were applied to detect the effect of GATA3-AS1 knockdown and overexpression on cell growth and migration of HCC. RT-qPCR was performed for the detection of the phosphatase and tensin homolog (PTEN), cyclin-dependent kinase inhibitor 1A (CDKN1A), and tumor protein p53 (TP53) expression in HCC cells after GATA3-AS1 knockdown and overexpression. Results GATA3-AS1 was significantly upregulated in HCC tissues compared with matched normal tissues. The high expression of GATA3-AS1 was significantly correlated with larger tumor size, advanced TNM stage, and more lymph node metastasis. High GATA3-AS1 expression was markedly correlated with shorter overall survival times of HCC patients. Furthermore, knockdown of GATA3-AS1 obviously inhibited Hep3B and HCCLM3 cell growth and migration, whereas overexpression of GATA3-AS1 had the opposite effects. In addition, GATA3-AS1 knockdown resulted in upregulated expression levels of tumor-suppressive genes, PTEN, CDKN1A, and TP53, in Hep3B and HCCLM3 cells, while restoration of GATA3-AS1 decreased PTEN, CDKN1A, and TP53 expression levels. Conclusion Our data suggested that GATA3-AS1 promotes cell proliferation and metastasis of HCC by suppression of PTEN, CDKN1A, and TP53.

Introduction
Hepatocellular carcinoma (HCC) is one of the most common diagnosed malignancies and the second leading cause of tumor-related deaths worldwide [1]. According to the global cancer statistics, approximately 500,000 new cases and 600,000 cases of mortalities occur every year [2]. e longterm prognosis of HCC remains very poor, with a low 5overall survival rate of 35% [3]. Surgical removal of the tumor and liver transplant are currently the most applicable options for HCC; however, tumor metastasis and recurrence are extremely common in postoperative HCC patients [4,5]. At present, because of the poor understanding of pathological molecular mechanisms in HCC, the effective therapy for this cancer is very limited [6]. erefore, it is essential to identify the mechanisms that underlie HCC metastasis for the development of novel sensitive and effective therapies.
Long noncoding RNAs (lncRNAs) are a class of noncoding RNAs which surpass 200 nucleotides in length [7]. According to the chromosome region to their adjacent coding genes, lncRNAs can be divided into five categories, including sense, antisense, bidirectional, intronic, and intergenic lncRNAs [8]. Accumulating data demonstrated that lncRNAs play vital roles in various biological processes, such as cell growth and differentiation, immune activation/ inactivation, and transcriptional and posttranscriptional regulation [9]. Recent studies have identified that some of lncRNAs were closely participated in tumorigenesis and metastasis and can be used as the effective biomarkers for HCC [10,11]. erefore, more and more researchers are keen on the eyes of lncRNAs in the progression of HCC.
In recent years, antisense lncRNAs have been identified to regulate cell proliferation, migration, and invasion in cancer [12]. For example, lncRNA antisense transcript of coding gene PCNA, PCNA-AS1, is shown to increase HCC cell growth [13]. e lncRNA MDC1 antisense RNA 1 (MDC1-AS1) associates with the development of bladder cancer [14]. e lncRNA AFAP1 antisense RNA 1, AFAP1-AS1, plays an oncogenic role in promoting cell migration in non-small cell lung cancer [15]. Besides, lncRNA DLX6 antisense RNA 1 (DLX6-AS1) promotes malignant phenotypes of gastric cancer cells [16]. e GATA3 antisense RNA 1 (GATA3-AS1) is a new antisense lncRNA, which is associated with T-cell development and differentiation [17]. It was later confirmed that GATA3-AS1 regulates GATA3 transcription in T-helper 2 cells [18]. However, the expression levels and function of GATA3-AS1 in HCC remain unknown. In this study, we firstly determined GATA3-AS1 expression in HCC tissues and cells. en, we performed in vitro loss-and gain-of-function experiments for GATA3-AS1 in the progression of HCC. is study highlighted the oncogenic role of GATA3-AS1 in regulating HCC cell proliferation and metastasis.

RT-qPCR.
Total RNA was extracted from HCC tissues and cells using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. e eligible RNA (OD 260/280 at 1.8-2.0) was converted to cDNA using the M-MLV Reverse Transcriptase kit (Toyobo, Osaka, Japan). After that, qPCR was carried out using a SYBR_Premix ExTaq II kit (Toyobo) on Applied Biosystems 7500 Real-Time PCR system (Applied Biosystems, Foster City, CA) to quantify the relative expression of target genes. e specific primers used for the amplification were shown in Table 2. ACTB (actin beta) was used as the internal control. e average of three independent data for each gene was calculated using 2 − ΔΔCq method [20].

Cell Proliferation Assay.
e proliferation ability of HCC cells was detected using the cell counting kit-8 (CCK-8) assay (Dojindo Laboratories, Kumamoto, Japan). About 6000 cells of Hep3B and HCCLM3 were plated in 96-well plates per well and cultured in the RPMI 1640 medium for 24 h. en, cells were treated with 50 pmol GATA3-AS1 siRNA/control siRNA or 0.5 μg pcDNA3.1 + GATA3-AS1 vector/empty vector using lipofectamine 2000 transfection reagent, according to the manufacturer's instructions. 10 μl CCK-8 solution/well was added, and cell proliferation ability was determined 0, 24, 48, and 72 h after transfection. e numbers of proliferative cells per well were measured at the absorbance (480 nm) by using a microplate reader (Bio-Rad, Hercules, CA, USA) at the indicated time points.
2.6. Wound Scratch Assay. Wound scratch assay was used to evaluate the metastatic ability of Hep3B and HCCLM3 cell lines in vitro. Approximately 8 × 10 6 cells were seeded into per 6-well plates and transfected with 200 pmol GATA3-AS1 siRNA/control siRNA or 4 μg pcDNA3.1 + GATA3-AS1 vector/empty vector. After 6 h transfection, the cell monolayer was scraped using a yellow pipette tip. e beginning gap length at 0 h and the residual gap length at 12 h were detected using the Image Analysis and Detection System (MIAS) (Leica Microsystems GmbH, Wetzlar, Germany). e experiments were conducted in triplicate and repeated at least three times and then analyzed by at least two observers with a double-blind manner.

Statistical
Analysis. Data were presented as mean ± SD (standard deviation) of three independent experiments. e statistical analyses were performed using SPSS 17.0 (SPSS Inc., Chicago, IL, USA). e expression differences between HCC tissues and matched normal tissues were analyzed using paired student's t-test. Chi-squared (χ 2 ) test was used for correlation analysis. Survival curves were plotted using the Kaplan-Meier method and were compared via the logrank test. CCK-8 assay was analyzed using one way-ANOVA following Bonferroni's post hoc test. e value of P < 0.05 was considered to be a statistically significant difference.

e GATA3-AS1 Expression Levels
Are Markedly Upregulated in HCC Tissues and Cell Lines. In order to identify the  Gene Sense (5′-3′) Antisense (5′-3′)  GATA3-AS1  TTGTTCCCTCTTCGCTCCT  TTGTTCCTTCACCGCATG  PTEN  ACCATAACCCACCACAGC  CAGTTCGTCCCTTTCCAG  CDKN1A  TCTACATCTTCTGCCTTAG  AAATGCCCAGCACTCTTA  TP53  TAAGGGTTAGTTTACAATC  TGCCAGCATTTCACAGAT  ACTB ACTTAGTTGCGTTACACC GTCACCTTCACCGTTCCA clinical significance of GATA3-AS1 in HCC patients, we firstly determined the GATA3-AS1 expression in HCC specimens. As shown in Figure 1(a), the GATA3-AS1 expression was significantly upregulated in HCC tissues compared with matched normal tissues (P < 0.05). We then analyzed the expression levels of GATA3-AS1 in two human HCC cells (Hep3B and HCCLM3) and the normal liver cell line (HL-7702). Consistent with the data of HCC specimens, GATA3-AS1 expression was obviously increased in the Hep3B and HCCLM3 cells compared with the HL-7702 cells (Figure 1(b), P < 0.05). e results indicated that upregulation of GATA3-AS1 may associate with the progression of HCC.

Upregulation of GATA3-AS1 Is Associated with the Aggressive Phenotypes and Poor Prognosis in HCC Patients.
e 80 pairs of HCC cases were divided into two groups based on the mean value of relative GATA3-AS1 expression levels, including low (n � 32) and high (n � 48) GATA3-AS1 expression groups. e χ 2 test was used to analyze the correlation between GATA3-AS1 expression and clinicopathologic variables. As shown in Table 1, the high level of GATA3-AS1 expression was strongly correlated with larger tumor size (P � 0.006), advanced TNM stage (P � 0.003), and more lymph node metastasis (P < 0.001) but not correlated with patient's gender (P � 0.506), age (P � 0.168), differentiation (P � 0.372), vascular invasion (P � 0.126), and liver cirrhosis (P � 0.920). Furthermore, Kaplan-Meier survival analysis showed a clear negative correlation between GATA3-AS1 expression and overall survival of HCC patients ( Figure 2). e patients with high levels of GATA3-AS1 expression had a significantly shorter overall survival times compared with those with low GATA3-AS1 expression (P < 0.001). ese results indicated that high expression of GATA3-AS1 is associated with the aggressive phenotypes and poor prognosis of HCC patients.

GATA3-AS1 Facilitates Cell Metastasis in Hep3B and
HCCLM3 Cells. Wound scratch assay was used to evaluate metastatic ability in HCC cells. e distance of cell migration in Hep3B cells transfected with GATA3-AS1 siRNA was significantly shorter compared with cells treated with control siRNA ( Figure 5(a), P < 0.05). Similar results were found in HCCLM3 cells after transfection of GATA3-AS1 siRNA/ control siRNA ( Figure 5(a), P < 0.05). By contrast, Hep3B cells transfected with pcDNA3.1 + GATA3-AS1 vector showed longer distance of cell migration compared with cells treated with empty vector (Figure 5(b), P < 0.05). Similar results were also shown in HCCLM3 cells after transfection of pcDNA3.1 + GATA3-AS1 vector/empty vector ( Figure 5(b), P < 0.05). ese data demonstrated that GATA3-AS1 promotes cell metastasis in HCC.

Discussion
Recent research studies suggested that mammalian transcriptome is more complex than protein-coding genes, and global transcriptome analysis provides detail evidences that a large proportion of human genome can generate transcripts from both strands [24]. More and more studies recently have demonstrated that lncRNAs participate in the regulation of cellular growth, metabolism, apoptosis, invasion, and metastasis [25]. For example, a liver-enriched lncRNA, liver-specific triglyceride regulator (LSTR), can regulate systemic lipid metabolism in mice [26]. Downregulation of lncRNA maternally expressed 3 (MEG3) has an effect on suppressing trophoblast cell migration and promoting apoptosis [27]. e lncRNA H19 imprinted maternally expressed transcript (H19) is significantly upregulated in hepatocellular carcinoma, and confers growth, migration, and invasion advantages on cancer cells [28]. Evidences have indicated that the antisense lncRNAs are participated in complicate pathophysiological processes of human diseases [29]. us, we hypothesized that antisense lncRNAs may play crucial roles in progression of HCC.
GATA3-AS1 is a newly antisense lncRNAs, which first identified from human CD4+ T-cell subsets [30]. Subsequently, Zhu et al. [31] applied microarray to analyze lncRNAs expression profiles in human bladder cancer and matched normal bladder tissues and found GATA3-AS1 is remarkably upregulated in tumor tissues. Recently, Gibbons et al. [18] demonstrated that GASTA3-AS1 significantly contributes to TH2 development. However, the expression pattern and role of GATA3-AS1 are largely unknown in HCC until now. In this study, we identified that the GATA3-AS1 expression is commonly upregulated in HCC tissues and Hep3B and HCCLM3 cells, indicating that GATA3-AS1 might have an important function in the progression of HCC. Besides, the elevated GATA3-AS1 expression was  closely correlated with larger tumor size, advanced TNM stage, and more lymph node metastasis, suggesting that upregulation of GATA3-AS1 represented an aggressive phenotype of HCC. Subsequently, in vitro loss-and gain-offunction experiments revealed that overexpression of GATA3-AS1 contributed to HCC cell proliferation and metastasis, whereas knockdown of GATA3-AS1 inhibited cell growth and metastasis. ese data strongly demonstrated that GATA3-AS1 acts as an oncogenic role in the progression of HCC.
TP53 is known as a tumor suppressor gene in various types of human cancer, and low expression of TP53 is associated with tumor progression and poor prognosis of HCC [32,33]. PTEN is a plasma membrane lipid phosphatase, which is identified as a tumor suppressor gene in multiple types of neoplasm [34]. In recent years, numerous previous TP53 expression. All of these data sufficiently demonstrated that GATA3-AS1 promotes cell proliferation and metastasis in HCC by suppression of PTEN, CDKN1A, and TP53. Some limitations of the study have to be improved in the further research. First, the HCC specimens used in our study are small. Second, how GATA3-AS1 regulates PTEN, CDKN1A, and TP53 remains unclear. Moreover, it is essential to study the role of GATA3-AS1 in more other gastrointestinal cancers in further work, such as gastric cancer, pancreatic cancer, gallbladder cancer, and colorectal cancer.
In conclusion, our results suggested that GATA3-AS1 promotes cell proliferation and metastasis in HCC by suppression of PTEN, CDKN1A, and TP53. Our findings contribute to a better understanding of the importance of GATA3-AS1 in HCC progression and provide a promising lncRNA-based targeted approach for HCC treatment.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
e authors have not declared any conflicts of interest.


Characterization and SSR identify of the complete chloroplast genome of Paphiopedilum concolor (Orchidaceae)

Abstract Paphiopedilum concolor (Orchidaceae) is endemic to China with highly ornamental and horticultural value. However, its whole chloroplast (cp) genome has not been reported yet. Here, the first nucleotide sequence of P. concolor was identified using Illumina sequencing technology. The circular genome is 166,744 bp in size and comprises a pair of inverted repeat (IR) regions of 15,694 bp, a large single-copy (LSC) region of 139,351 bp, and a small single-copy (SSC) region of 11,729 bp. The total GC content is 34.6%, whereas the corresponding values of the LSC, SSC, and IR regions are 33.7%, 26.0%, and 41.6%, respectively. The chloroplast genome contains 137 genes, including 91 protein-coding genes, eight ribosomal RNA genes, and 38 transfer RNA genes. Moreover, we identified a total of 129 microsatellites (SSRs) within the P. concolor cp genome. These newly developed genomic resources will greatly important to the further conservation genetic studies of Paphiopedilum species.

Paphiopedilum concolor; chloroplast genome; microsatellites; MITObim
Paphiopedilum concolor (Orchidaceae), popularly known as slipper orchids, native to southeast Burma, southwest China, Thailand, and Laos. Due to destruction of their survival space and human over-exploitation for horticulture (Liu et al. 2006), P. concolor is identified as endangered class by the IUCN (International Union for Conservation of Nature, http://www. iucnredlist.org/). To facilitate its genetic research and contribute to its conservation, we assembled the cp genome of P. concolor with shotgun genome sequences obtained with the Illumina HiSeq platform. Furthermore, the microsatellite markers of P. concolor were analyzed for application of the cp sequences. These newly developed genomic data here will greatly be contributed to the genetic studies of Paphiopedilum in the future.
DNA samples were extracted from the fresh leaves that were collected from a single individual of P. concolor at Qipan mountain in Kunming, Yunnan Province (25 02'35N, 102 31'52E) with CTAB method (Doyle and Doyle 1987) and stored in our lab. The whole genome shotgun sequencing of P. concolor was performed by Beijing Gene Institution (BGI, Shenzhen, China) using the Illumina HiSeq 2000 platform (Illumina, Hayward, CA). Total 67.5 M 125 bp raw reads were retrieved and trimmed using CLC Genomics Workbench v8.0 (CLC Bio, Aarhus, Denmark). A subset of 34.9 M trimmed reads were used for reconstructing the chloroplast genome by MITObim v1.8 (Hahn et al. 2013), with that of its congener Paphiopedilum armeniacum (GenBank: NC_026779.1) as the initial reference genome. A total of 156,484,607 individual chloroplast reads yielded an average coverage of 606.4-fold. The chloroplast genome was annotated in GENEIOUS R11 (Biomatters Ltd., Auckland, New Zealand).
The chloroplast genome of P. concolor is a doublestranded circular DNA molecule with 166,744 bp in size (MH191340). It comprises a pair of inverted repeat (IR) regions of 15,694 bp each, separated by a large single-copy (LSC) region of 139,351 bp, and a small single-copy (SSC) region of 11,729 bp. The total GC content is 34.6%, whereas the corresponding values of the LSC, SSC, and IR regions are 33.7%, 26.0% and 41.6%, respectively.
This chloroplast genome harbors 137 functional genes, including 91 protein-coding genes (PCGs), 38 tRNA genes, and eight rRNA genes. 35 PCGs, 17 tRNA genes, and four rRNA genes are located in the forward strand, whereas others are located in the reverse strand. Among them, 38 genes are involved in photosynthesis, six genes are in substance metabolism, and 29 genes are related with self-replication. Three genes that are ndhA and ndhE are pseudogenized or lost in its cpDNA when compared with that of a closed species, P. dianthum and P. armeniacum (Liu et al. 2006;Hou et al. 2017). Phylogenetic tree of 38 species within the family Orchidaceae based on the Maximum-Likelihood analysis of the 45 shared PCGs sequences using 500 bootstrap replicates using MEGA 6.0 (Tamura et al. 2013), which showed the position of P. concolor was situated as the sister of P. niveum in Orchidaceae (Figure 1).
Additionally, a total of 129 microsatellites (SSRs) were identified in the P. Concolor cp genome using MISA. Among them, 97 were located in the LSC regions, whereas 12 and eight were found in the IR and SSC regions, respectively. Among these SSRs, 112 were mononucleotides, seven were dinucleotides, and 10 were tetranucleotides, and more than half were composed of A or T bases. Our findings will provide a foundation for further investigation of chloroplast genome evolution in Paphiopedilum.


extfieldsofstudy: Biology Medicine

text:
Complexity of the Basic Reproduction Number (R0)

The basic reproduction number (R0), also called the basic reproduction ratio or rate or the basic reproductive rate, is an epidemiologic metric used to describe the contagiousness or transmissibility of infectious agents. R0 is affected by numerous biological, sociobehavioral, and environmental factors that govern pathogen transmission and, therefore, is usually estimated with various types of complex mathematical models, which make R0 easily misrepresented, misinterpreted, and misapplied. R0 is not a biological constant for a pathogen, a rate over time, or a measure of disease severity, and R0 cannot be modified through vaccination campaigns. R0 is rarely measured directly, and modeled R0 values are dependent on model structures and assumptions. Some R0 values reported in the scientific literature are likely obsolete. R0 must be estimated, reported, and applied with great caution because this basic metric is far from simple.

T he basic reproduction number (R 0 ), pronounced "R naught," is intended to be an indicator of the contagiousness or transmissibility of infectious and parasitic agents. R 0 is often encountered in the epidemiology and public health literature and can also be found in the popular press (1-6). R 0 has been described as being one of the fundamental and most often used metrics for the study of infectious disease dynamics (7)(8)(9)(10)(11)(12). An R 0 for an infectious disease event is generally reported as a single numeric value or low-high range, and the interpretation is typically presented as straightforward; an outbreak is expected to continue if R 0 has a value >1 and to end if R 0 is <1 (13). The potential size of an outbreak or epidemic often is based on the magnitude of the R 0 value for that event (10), and R 0 can be used to estimate the proportion of the population that must be vaccinated to eliminate an infection from that population (14,15). R 0 values have been published for measles, polio, influenza, Ebola virus disease, HIV disease, a diversity of vectorborne infectious diseases, and many other communicable diseases (14,(16)(17)(18).
The concept of R 0 was first introduced in the field of demography (9), where this metric was used to count offspring. When R 0 was adopted for use by epidemiologists, the objects being counted were infective cases (19). Numerous definitions for R 0 have been proposed. Although the basic conceptual framework is similar for each, the operational definitions are not always identical. Dietz states that R 0 is "the number of secondary cases one case would produce in a completely susceptible population" (19). Fine supplements this definition with the description "average number of secondary cases" (17). Diekmann and colleagues use the description "expected number of secondary cases" and provide additional specificity to the terminology regarding a single case (13).
In the hands of experts, R 0 can be a valuable concept. However, the process of defining, calculating, interpreting, and applying R 0 is far from straightforward. The simplicity of an R 0 value and its corresponding interpretation in relation to infectious disease dynamics masks the complicated nature of this metric. Although R 0 is a biological reality, this value is usually estimated with complex mathematical models developed using various sets of assumptions. The interpretation of R 0 estimates derived from different models requires an understanding of the models' structures, inputs, and interactions. Because many researchers using R 0 have not been trained in sophisticated mathematical techniques, R 0 is easily subject to misrepresentation, misinterpretation, and misapplication. Notable examples include incorrectly defining R 0 (1) and misinterpreting the effects of vaccination on R 0 (3). Further, many past lessons regarding this metric appear to have been lost or overlooked over time. Therefore, a review of the concept of R 0 is needed, given the increased attention this metric receives in the academic literature (20). In this article, we address misconceptions about R 0 that have proliferated as this metric has become more frequently used outside of the realm of mathematical biology and theoretic epidemiology, and we recommend that R 0 be applied and discussed with caution.

Variations in R 0
For any given infectious agent, the scientific literature might present numerous different R 0 values. Estimations of the R 0 value are often calculated as a function of 3 primary parameters-the duration of contagiousness after a person becomes infected, the likelihood of infection per contact between a susceptible person and an infectious person or vector, and the contact rate-along with additional parameters that can be added to describe more complex cycles of transmission (19). Further, the epidemiologic triad (agent, host, and environmental factors) sometimes provides inspiration for adding parameters related to the availability of public health resources, the policy environment, various aspects of the built environment, and other factors that influence transmission dynamics and, thus, are relevant for the estimation of R 0 values (21). Yet, even if the infectiousness of a pathogen (that is, the likelihood of infection occurring after an effective contact event has occurred) and the duration of contagiousness are biological constants, R 0 will fluctuate if the rate of human-human or human-vector interactions varies over time or space. Limited evidence supports the applicability of R 0 outside the region where the value was calculated (20). Any factor having the potential to influence the contact rate, including population density (e.g., rural vs. urban), social organization (e.g., integrated vs. segregated), and seasonality (e.g., wet vs. rainy season for vectorborne infections), will ultimately affect R 0 . Because R 0 is a function of the effective contact rate, the value of R 0 is a function of human social behavior and organization, as well as the innate biological characteristics of particular pathogens. More than 20 different R 0 values (range 5.4-18) were reported for measles in a variety of study areas and periods (22), and a review in 2017 identified feasible measles R 0 values of 3.7-203.3 (23). This wide range highlights the potential variability in the value of R 0 for an infectious disease event on the basis of local sociobehavioral and environmental circumstances.
Various Names for R 0 Inconsistency in the name and definition of R 0 has potentially been a cause for misunderstanding the meaning of R 0 . R 0 was originally called the basic case reproduction rate when George MacDonald introduced the concept into the epidemiology literature in the 1950s (17,19,24,25). Although MacDonald used Z 0 to represent the metric, the current symbolic representation (R 0 ) appears to have remained largely consistent since that time. However, multiple variations of the name for the concept expressed by R 0 have been used in the scientific literature, including the use of basic and case as the first word in the term, reproduction and reproductive for the second word, and number, ratio, and rate for the final part of the term (13). Although the frequent use of the term basic reproduction rate is in line with MacDonald's original terminology (9), some users interpret the use of the word rate as suggesting a quantity having a unit with a per-time dimension (7). If R 0 were a rate involving time, the metric would provide information about how quickly an epidemic will spread through a population. But R 0 does not indicate whether new cases will occur within 24 hours after the initial case or months later, just as R 0 does not indicate whether the disease produced by the infection is severe. Instead, R 0 is most accurately described in terms of cases per case (7,13). Calling R 0 a rate rather than a number or ratio might create some undue confusion about what the value represents.

R 0 and Vaccination Campaigns
Vaccination campaigns reduce the proportion of a population at risk for infection and have proven to be highly effective in mitigating future outbreaks (26). This conclusion is sometimes used to suggest that an aim of vaccination campaigns is to remove susceptible members of the population to reduce the R 0 for the event to <1. Although the removal of susceptible members from the population will affect infection transmission by reducing the number of effective contacts between infectious and susceptible persons, this activity will technically not reduce the R 0 value because the definition of R 0 includes the assumption of a completely susceptible population. When examining the effect of vaccination, the more appropriate metric to use is the effective reproduction number (R), which is similar to R 0 but does not assume complete susceptibility of the population and, therefore, can be estimated with populations having immune members (16,20,27). Efforts aimed at reducing the number of susceptible persons within a population through vaccination would result in a reduction of the R value, rather than R 0 value. In this scenario, vaccination could potentially end an epidemic, if R can be reduced to a value <1 (16,27,28). The effective reproduction number can also be specified at a particular time t, presented as R(t) or R t , which can be used to trace changes in R as the number of susceptible members in a population is reduced (29,30). When the goal is to measure the effectiveness of vaccination campaigns or other public health interventions, R 0 is not necessarily the best metric (10,20).

Measuring and Estimating R 0
Counting the number of cases of infection during an epidemic can be extremely difficult, even when public health officials use active surveillance and contact tracing to attempt to locate all infected persons. Although measuring the true R 0 value is possible during an outbreak of a newly emerging infectious pathogen that is spreading through a wholly susceptible population, rarely are there sufficient data collection systems in place to capture the early stages of an outbreak when R 0 might be measured most accurately. As a result, R 0 is nearly always estimated retrospectively from seroepidemiologic data or by using theoretical mathematical models (31). Data-driven approaches include the use of the number of susceptible persons at endemic equilibrium, average age at infection, final size equation, and intrinsic growth rate (10). When mathematical models are used, R 0 values are often estimated by using ordinary differential equations (8-10,19,31), but high-quality data are rarely available for all components of the model. The estimated values of R 0 generated by mathematical models are dependent on numerous decisions made by the modeler (8,32,33). The population structure of the model, such as the susceptible-infectious-recovered model or susceptibleexposed-infectious-recovered model, which includes compartments for persons who are exposed but not yet infectious, as well as assumptions about demographic dynamics (e.g., births, deaths, and migration over time), are critical model parameters. Population mixing and contact patterns must also be considered; for example, for homogeneous mixing, all population members are equally likely to come into contact with one another, and for heterogeneous mixing, variation in contact patterns are present among age subgroups or geographic regions. Other decisions include whether to use a deterministic (yielding the same outcomes each time the model is run) or stochastic (generating a distribution of likely outcomes on the basis of variations in the inputs) approach and which distributions (e.g., Gaussian or uniform distributions) to use to describe the probable values of parameters, such as effective contact rates and duration of contagiousness. Furthermore, many of the parameters included in the models used to estimate R 0 are merely educated guesses; the true values are often unknown or difficult or impossible to measure directly (31,34,35). This limitation is compounded as models become more complex and, thus, require more input parameters (20,35), such as when using models to estimate the value of R 0 for infectious pathogens with more complex transmission pathways, which can include vectorborne infectious agents or those with environmental or wildlife reservoirs. In summary, although only 1 true R 0 value exists for an infectious disease event occurring in a particular place at a particular time, models that have minor differences in structure and assumptions might produce different estimates of that value, even when using the same epidemiologic data as inputs (20,31,32,36,37).

Obsolete R 0 Values
New estimates of R 0 have been produced for infectious disease events that occurred in recent history, such as the West Africa Ebola outbreak (34,38,39). However, for many vaccine-preventable diseases, the scientific literature reports R 0 values calculated much further back in history. For example, the oft-reported measles R 0 values of 12-18 are based on data acquired during 1912-1928 in the United States (R 0 of 12.5) and 1944-1979 in England and Wales (R 0 of 13.7-18.0) (14), even though more recent estimates of the R 0 for measles highlight a much greater numeric range and variation across settings (23). For pertussis (R 0 of 12-17), the original data sources are 1908-1917 in the United States (R 0 of 12.2) and 1944-1979 in England and Wales (R 0 of 14.3-17.1) (14). The major changes that have occurred in how humans organize themselves both socially and geographically make these historic values extremely unlikely to match present day epidemiologic realities. Behavioral changes undoubtedly have altered contact rates, which are a key component of R 0 calculations. Yet, these R 0 values have been repeated so often in the literature that newer R 0 values generated by using modern data might be dismissed if they fall outside the range of previous estimates. Given that R 0 is often considered when designing and implementing vaccination strategies and other public health interventions, the use of R 0 values derived from older data is likely inappropriate (23). Decisions about public health practice should be made with contemporaneous R 0 values or R values instead.

Conclusions
Although R 0 might appear to be a simple measure that can be used to determine infectious disease transmission dynamics and the threats that new outbreaks pose to the public health, the definition, calculation, and interpretation of R 0 are anything but simple. R 0 remains a valuable epidemiologic concept, but the expanded use of R 0 in both the scientific literature and the popular press appears to have enabled some misunderstandings to propagate. R 0 is an estimate of contagiousness that is a function of human behavior and biological characteristics of pathogens. R 0 is not a measure of the severity of an infectious disease or the rapidity of a pathogen's spread through a population. R 0 values are nearly always estimated from mathematical models, and the estimated values are dependent on numerous decisions made in the modeling process. The contagiousness of different historic, emerging, and reemerging infectious agents cannot be fairly compared without recalculating R 0 with the same modeling assumptions. Some of the R 0 values commonly reported in the literature for past epidemics might not be valid for outbreaks of the same infectious disease today. R 0 can be misrepresented, misinterpreted, and misapplied in a variety of ways that distort the metric's true meaning and value. Because of these various sources of confusion, R 0 must be applied and discussed with caution in research and practice. This epidemiologic construct will only remain valuable and relevant when used and interpreted correctly.


extfieldsofstudy: Medicine Chemistry

text:
Optimal diameter reduction ratio of acinar airways in human lungs

In the airway network of a human lung, the airway diameter gradually decreases through multiple branching. The diameter reduction ratio of the conducting airways that transport gases without gas exchange is 0.79, but this reduction ratio changes to 0.94 in acinar airways beyond transitional bronchioles. While the reduction in the conducting airways was previously rationalized on the basis of Murray’s law, our understanding of the design principle behind the acinar airways has been far from clear. Here we elucidate that the change in gas transfer mode is responsible for the transition in the diameter reduction ratio. The oxygen transfer rate per unit surface area is maximized at the observed geometry of acinar airways, which suggests the minimum cost for the construction and maintenance of the acinar airways. The results revitalize and extend the framework of Murray’s law over an entire human lung.

Introduction
Fluid transport systems in the form of branching networks have evolved in multicellular organisms to deliver bulk metabolic matter to matter exchange sites [1][2][3][4][5][6][7]. In a branching network, a mother branch is divided into numerous terminal daughters. The aggregate cross-sectional area of the vessels of a single generation generally increases with branch generations, and the flow velocity thus decreases. A low flow speed is advantageous for allowing more time for mass transfer at the terminal branches [1]. For instance, the diameter of vascular vessels in the human body decreases from~1 cm at the aortae to~10 μm at the capillaries, whereas the aggregate cross-sectional area increases from~1 cm 2 to~10 3 cm 2 [1,8]. However, expanding the cross-sectional area of the daughter vessels can be costly because of the construction and maintenance of redundant channels. Murray's law explains how the costs of running the vascular system can be minimized by controlling the diameter reduction ratio [9,10]. The same framework has been utilized for rationalizing the observed diameter reduction ratio in the a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 xylem in plants, for which the constructing cost of conduits is the primary factor limiting the expansion of the cross-sectional area of the xylem vessels [11].
The airway system in a human lung exhibits a similar branching architecture. A single trachea bifurcates into~2 23 terminal branches. In this bronchial network, the expansion of the aggregate cross-sectional area of the daughter channels is limited by the airway volume, in such a way that the air transport to the alveoli is maximized for a given amount of inhalation air [12,13]. Nevertheless, this rationale explains the airway branching only in conducting airways where no gas exchange occurs, as shown in Fig 1. It has been supposed that the change in gas transfer mode from advection to diffusion is responsible for the transition in the reduction ratio of bronchial airways [10,14,15]. Explicit analytical or computational studies, often combined with fractal modeling, focused on the velocity field in airways [16], permeability in acinus [17], breathing irregularity [18], and asymmetric branching [19][20][21][22]. The principle of the cost minimization for diffusive mass transfer was developed, which successfully provided the rationale for the spiracle pore networks of insects [23,24]. However, this model cannot be directly applied to acinar airways because the observed diameter reduction ratio of acinar airways (k = 0.94) is much larger than that of Murray's law for diffusion (k = 0.71).
Here we present a model for the hitherto unexplained diameter reduction ratio in the acinar airways, k = 0.94. With a simplified airway geometry that is amenable to mathematical analysis, our model captures an essential physical picture responsible for the observed diameter reduction ratio.

Results
We begin with an analysis of the oxygen transfer in human lung airways. During a 2 s period of inhalation, a negative pressure in the pleural cavity induces the expansion of alveoli, and the  [12], and an average diameter of alveoli is 200 μm [34]. (B) Reduction in normalized airway diameter along with airway generation. The diameter reduction ratio is 0.79 in the conducting airways, whereas it shifts to 0.94 in the acinar airways. Note that Murray's law for diffusion in insects, k = 0.71, cannot explain the acinar airways reduction ratio (red dashed line). Data were taken from Finlay [8] and Weibel [14]. total volume increase reaches approximately 500 ml [25]. As a result of the increase in the cross-sectional area of the airways, the average speed of the air flow decreases from about 0.5 m/s in the trachea to 1 μm/s in the alveoli. The dominant oxygen transfer mechanism can be examined using the Peclet number, Pe = ul/D, the ratio of the advective to the diffusive mass transfer rates, where l is the length of a single airway branch (l~1 mm), u is the speed of the air flow, and D is the oxygen diffusion coefficient in air (D~0.2 cm 2 /s). Using the data of the cross-sectional area of airways for each generation [8,14], one can find that Pe < 1 after transitional bronchioles, which suggests a shift in the dominant oxygen transfer mechanism from advection to diffusion [10,15].
We develop a mathematical model of the oxygen transport in the acinar airways. Since the oxygen transfer through the channels via diffusion depends on their cross-sectional area, a trumpet model can be used [26]. We construct a geometric model of the acinar airways as a stepwise channel with a unit depth by arranging all the channels side by side in such a way as to retain an equivalent aggregate cross-sectional area for each generation, as shown in Fig 2. The stepwise channel is further simplified as a diverging duct enclosed by two curved boundaries. Assuming that the transition of diameter reduction ratio occurs near the 16th branch, we can express the cross-sectional area as A(x) = A 16 e x/a with a = l/ln(2k 2 ), where x is the distance from the 16th branch, A 16 is the total cross-sectional area of the acinar airways at the 16th generation, and k is the diameter reduction ratio.
Alveoli mediate oxygen transfer from the airways to the capillaries. Alveoli begin to appear from the transitional bronchioles, but most alveoli are attached to distal airways. Fig 3 shows the cumulated alveolar surface area with respect to airway generation [14,17]. Assuming that the airway length in each generation is 1 mm, we formulate the cumulated alveolar surface area as a continuous function A a (x) using spline interpolation, and the local alveolar surface area per unit length is given by A a 0 (x) = dA a /dx. One can calculate the local oxygen transfer rate through alveoli per length as q 0 ðxÞ ¼ bD w ðφðxÞ À φ c ÞA 0 a ðxÞ=h, where β is the solubility of oxygen in water [17], D w is the diffusivity of oxygen in air, φ(x) is the partial pressure of oxygen in the acinar airways, φ c is the oxygen partial pressure in the blood capillary, and h is the thickness of the alveolar membrane.
We examine the oxygen conservation in an infinitesimal control volume, as shown in Fig 2. Because sidewalls of a single airway prevent diffusion to neighboring airways, we only consider longitudinal diffusion along the x-axis, where t is the time after inhalation begins. Solving Eq (1) requires an initial condition and two boundary conditions. We assume that the oxygen partial pressure in the acinar airways is the same as that of the blood capillary when inhalation begins because an exhalation time of~2 s is greater than the diffusion time scale L 2 /(4D)~0.8 s, where L~8 mm is the diffusion length from the 16th to 23rd airways. Thus, the initial condition is written as φ(x, 0) = φ c . Once inhalation begins, fresh air is supplied from the conducting airways. Accordingly, we assume that the oxygen partial pressure at the inlet of the 16th airway branch remains the same as that in the fresh air during the inhalation. This leads us to write a boundary condition as φ(0,t) = φ a , where φ a is the oxygen partial pressure in the fresh air. The other boundary condition comes from the 23 rd generation where oxygen transfer is allowed only through the alveolar membrane, so that @φ @x À � x¼L ¼ 0. By numerically solving Eq (1), we obtain the profile of oxygen partial pressure in acinar airways during the inhalation (see Materials and methods). Fig 4A shows the dimensionless oxygen partial pressureφ ¼ ðφ À φ c Þ=ðφ a À φ c Þ as a function ofx ¼ x=L for k = 0.9, which approaches the steady state profile within~0.1 s. Hence, we neglect the unsteady effects in estimating the oxygen transfer during the whole inhalation process. Fig 4B and 4C show the profiles of the partial pressure and normalized oxygen transfer rateq to the blood capillaries, respectively, for various diameter reduction ratios (k = 0.7, 0.9 and, 1.1). Hereq is defined aŝ withÂ 0 a ¼ ðdA a =dxÞ=A a ðx ¼ 1Þ so that the total area below a curveq is equal to unity. Acinar airways comprise a thin layer of epithelial cells covering airways and alveoli. Therefore, building an additional airway demands costs for construction and maintenance of the epithelial cell layer, which are proportional to the total surface area of the acinar airways. This reminds us of the fact that the expansion of the cross-sectional area in plant xylem vessels is limited by the carbon investment for the construction of the vessels [6]. We thus suggest that the total surface area of the acinar airways is the primary limiting factor. Fig 5A displays the total oxygen transfer rate through the entire alveoli divided by the surface area of the acinar airways and alveoli versus the diameter reduction ratio. We see that although the total oxygen transfer rate increases with the diameter reduction ratio k, the sharp increase of the surface area with k results in the maximum Q/A at k = 0.94 (see Fig 5B). We thus infer that a ratio of 0.94 is the optimal reduction ratio to minimize the cost of the acinar airways for oxygen transfer. This theoretical value is indeed consistent with the biological data shown in Fig 1B. On the basis of the model, it can be deduced that the transition of the diameter reduction ratios between the conducting (k = 0.79) and acinar (k = 0.94) airways saves approximately 10% of the epithelial cell layers compared with the case without this transition. Consequently, our model suggests that this transition occurs for reducing energy and materials consumption in the acinar airways.

Discussion
The oxygen transfer to blood in acinar airways depends on the permeability of the alveolar membrane βD w /h, so that membrane permeability can change the optimal diameter reduction ratio. One can infer that the higher the permeability of the membrane, the less the surface required for a given amount of oxygen transfer. Hence, the more permeable alveoli would reduce the transfer through distal airways, referred to as 'screening effects' [17,27], and reduce the effectiveness of distal airways. In Fig 6, we present the dependence of the oxygen transfer rate per surface area on the diameter reduction ratio for various permeability of the alveolar membrane. Indeed, the optimal diameter reduction ratio decreases with the membrane permeability, resulting in a smaller area of the terminal airways for higher permeability.
Originally developed for blood vessels, Murray's law describes branching structure for minimizing energy cost for convective transport and metabolism of blood [9,10]. This design principle of branching structure has been used as a framework to understand various natural network systems under the assumption that natural branching networks have evolved in such a way as to minimize the cost for transferring a given quantity of matter [6,7]. Murray's law for conducting airways of lung explains the minimization of energy consumption for oxygen transport for a given volume of air inhalation [8,10,17]. Murray's law for plant xylem elucidates the minimization of the primary energetic cost caused by sap flow and vessel construction [11]. Interestingly, despite various forms of energetic costs, Murray's law for these networks commonly consider convection transport energy and vessel volume. Murray's law for diffusion on the other hand can be formulated mathematically by replacing the energy cost for convective transport with the energy cost for diffusive transport, leading to an optimal diameter reduction ratio of 0.71 [23,24].
However, the biological data of acinar airways in human lungs cannot be explained by either of the aforementioned models. We here raise in this report the question of transition of diameter reduction effects on lung optimization. Specifically, we consider the amount of diffusive transport of oxygen per surface area of airways. This implies that the dominant cost for the construction and maintenance of acinar airways comes from epithelial cell layers of airways and alveoli, which should be measured by the surface area rather than the volume.
Although advection is typically cost effective on the organismal scale, diffusion is a rapid, reliable, and cheap way to transfer matter on the cell scale [1,28]. In mammals relying on air breathing, the characteristic diameter of the terminal branches of the airways is on the order of 100 μm, regardless of the body size [14,29,30]. Thus, gas transport via diffusion would be more effective near the terminal branches. Accordingly, the transition of the oxygen transfer mode is also expected in the lung airways of other species, which may cause the transition of the diameter reduction ratios. Indeed, some anatomical data on the lung airways of other species, including rats, rabbits, and canines, show a transition in the diameter reduction ratio as in human lung airways [29,30], suggesting an interesting aspect worth pursuing in the future. This optimal strategy of acinar airways can guide design and construction of artificial networks where one needs to maximize fluid transport to a given area [31][32][33].

Materials and methods
We explain the process to solve Eq (1), the oxygen diffusion equation in the dichotomous hierarchical branch networks, subject to the boundary conditions: φð0; tÞ ¼ φ a and @φ @x Using the following dimensionless variableŝ we find dimensionless forms of Eqs (1) and (3): φðx; 0Þ ¼ 0;φð0;tÞ ¼ 1 and @φ @x jx ¼1 ¼ 0 Eq (4) with the initial and boundary conditions was solved numerically with parameters given in Table 1. Spatial derivatives in Eq (4) was discretized on uniformly spaced spatial grids (N = 100) using the second-order central difference scheme to obtain a system of ordinary differential equations (ODE) corresponding to a type of initial value problem. The resulting system of ODE is solved by a multistep method.