Due to its significant surface area and its good mechanical and thermal stability, graphene oxide has a plethora of applications in various scientific fields including heterogenous catalysis, gas storage, environmental remediation, etc. In analytical chemistry, graphene oxide has been successfully employed for the extraction and preconcentration of organic compounds, metal ions, and proteins. Since graphene oxide sheets are negatively charged in aqueous solutions, the material and its derivatives are ideal sorbents to bind with metal ions. To date, various graphene oxide nanocomposites have been successfully synthesized and evaluated for the extraction and preconcentration of metal ions from biological, environmental, agricultural, and food samples. In this review article, we aim to discuss the application of graphene oxide and functionalized graphene oxide nanocomposites for the extraction of metal ions prior to their determination via an instrumental analytical technique. Applications of ionic liquids and deep eutectic solvents for the modification of graphene oxide and its functionalized derivatives are also discussed.Graphene oxide is a compound with a form similar to graphene, composed of carbon atoms in a sp Among environmental pollutants, metals are of high importance due to their potential toxic effects and tendency to bioaccumulate in aquatic ecosystems. In small quantities, certain metals like iron, copper, cobalt, manganese, and zinc are nutritionally essential for a healthy life, while other metals such as mercury, arsenic, chromium, cadmium, and lead have no known role in biological systems and they are considered toxic and extremely dangerous even at trace levels ,2,3. TheA wide variety of novel sorbents including graphene, graphene oxide, carbon nanotubes, metal–organic frameworks, covalent organic frameworks, and zeolitic imidazole frameworks have been employed for the extraction of metal ions ,14,15,16Graphene is a non-polar hydrophobic carbon-based nanomaterial that was discovered by Geim et al. in 2004 . Since t2 hybridization linked within a single-atom layer octane chloride. Due to the modification with a double-charged IL, the novel sorbent exhibited high adsorption capacity and was successfully used for the extraction of Pb(II), Cd(II), Ni(II), Cu(II), and Cr(III) prior to their determination by FAAS.In 2016, Lotfi et al. synthesized a covalently bonded double-charged ionic liquid on magnetic graphene oxide as and used it for the ultrasound-assisted MSPE of heavy metals from medicine capsules . For thi3O4 using 1-hexadecyl-3-methylimidazolium chloride via an electrostatic self-assembly technique [Aliyari et al. modified GO/Feechnique . For thi3O4–SiO2–GO nanoparticles. The sorbent was employed for the extraction of Al(III), Cr(III), Cu(II), and Pb(II) from environmental water samples prior to their determination by ICP-OES [An amino-acid-derived ionic liquid was used for the coating of Fe ICP-OES . The res3O4 and 1-butyl-3-methylimidazolium hexafluorophosphate and used it for the preconcentration of Cu(II), Zn(II), Cd(II), Cr(III), Pb(II), and Co(II) ions from environmental water samples [3O4 nanoparticles, graphene oxide, and ionic liquid is an efficient sorbent for extraction of heavy metal ions from water prior to their determination by ICP-OES. The metal ions were complexed with 1-(2-pyridylazo)-2-naphthol and were further extracted by the sorbent. Subsequently, elution with acetonitrile was performed and the analytes were determined by ICP-OES. Compared with the free IL and GO/Fe3O4, the bucky gel showed higher extraction efficiency, indicating the appearance of a synergic effect in the IL/magnetic GO material.Rofouei et al. synthesized a magnetic bucky gel by mixing GO/Fe samples . The terDeep eutectic solvents (DESs) are systems formed from a eutectic mixture of Lewis or Brønsted acids and bases that contain a variety of anionic and/or cationic species . A DES tDue to their superior characteristics, such as low cost, easy preparation, low toxicity, tunability, low vapor pressure, biocompatibility, and biodegradability, DESs have a plethora of applications in various research areas. Among their applications are gas adsorption, biotransformation, purifying and manufacturing biodiesel, and metal-processing applications ,116.DESs exhibit an ionizable nature and strong hydrogen bonding, providing multiple benefits compared to traditional organic solvents. In the field of analytical chemistry, deep eutectic solvents have been used as a stationary phase for chromatography as well as mobile phases. DESs have been also successfully employed for microextraction of target analytes, modification of nanoparticles, or as eluents in sample preparation of various samples .3O4 nanoparticles. The sorbent was employed for the removal of mercury ions from wastewater samples. Due to the presence of –SH groups, extraction efficiency was significantly improved compared with the non-functionalized magnetic graphene oxide sorbent. Moreover, the functionalized sorbent exhibited good stability, since it was found to be reusable for at least 7 further applications after regeneration [Magnetic graphene oxide nanoparticles have been treated with DESs in order to create new efficient adsorbents. A ternary hydrosulphonyl-based deep eutectic solvent comprised of choline chloride/itaconic acid/3-mercaptopropionic acid (molar ratio 2:1:1) was used to functionalize GO/Feneration .3O4 nanoparticles have been also evaluated in order to take advantage of the ease in magnetic separation of Fe3O4 and the high extraction efficiency of GO.Due to their significant surface area and high mechanical and chemical stability, graphene-oxide-derived nanocomposites have been successfully employed for the extraction of a wide variety of metal ions from environmental, food, biological, and agricultural samples. Graphene oxide contains a significant number of oxygen-containing functional groups that are able to bind with multivalent metal ions through both electrostatic and coordinate approaches. Magnetic composites of GO with FeThe main drawback of these sorbents is the potential aggregation and restacking of the nanosheets, which might lead to blocking of the active adsorption sites of the sorbent. In order to overcome this limitation, a wide variety of functional groups have been proposed and evaluated. Functionalization has also been found to enhance the dispersibility of the sorbent, as well as the extraction efficiency of the sorbent and/or selectivity towards the target analytes.Finally, in order to enhance the extraction efficiency of the sorbent, modification of graphene oxide with ionic liquids and deep eutectic solvents has been proposed. In this case, the modification process is rapid, simple, and it does not require toxic organic solvents. The stability of the modified nanocomposites and their potential reusability are still relatively limited. However, compared with other novel sorbents such as metal–organic frameworks and zeolitic imidazole frameworks, GO nanocomposites exhibit higher reusability, meeting the requirements of green chemistry.Since most applications of graphene-oxide-derived materials are focused on MSPE and d-SPE, future advances include the extension of the application of these materials to more and alternative sample preparation formats, including PT-SPE and SBSE. Moreover, evaluation of more functional groups in order to enhance the extraction efficiency and selectivity of the extraction process should be performed. It can be concluded that with the new and the anticipated developments in the field of graphene-oxide-derived nanomaterials, it is expected that these will become a useful tool in the analytical repertoire for trace inorganic analysis in environmental, biological, and food applications.


A novel multifunctional device based on a hybrid metal–graphene Electromagnetically induced transparency (EIT) metamaterial at the terahertz band is proposed. It is composed of a parallel cut wire pair (PCWP) that serves as a dark mode resonator, a vertical cut wire pair (VCWP) that serves as a bright mode resonator and a graphene ribbon that serves as a modulator. An ultra-broadband transmission window with 1.23 THz bandwidth can be obtained. The spectral extinction ratio can be tuned from 26% to 98% by changing the Fermi level of the graphene. Compared with previous work, our work has superior performance in the adjustable bandwidth of the transmission window without changing the structure of the dark and bright mode resonators, and has a high extinction ratio and dynamic adjustability. Besides, we present the specific application of the device in filters and optical modules. Therefore, we believe that such a metamaterial structure provides a new way to actively control EIT-like, which has promising applications in broadband optical filters and photoelectric intensity modulators in terahertz communications. Metamaterials that serve as kinds of artificial materials have received tremendous concern in modern materials science and physics ,2 for thElectromagnetically induced transparency (EIT) is a significant phenomenon which comes from the destructive interference between different excitation pathways in a three-level atomic system ,13. It cEF ,23, and graphene . A lot ographene ,26,27 haIn this paper, we proposed a hybrid metal–graphene metamaterial in the terahertz spectrum for the realization of changing the EIT-like transmission window and adjusting the spectral extinction ratio. After integrating two graphene strips with different widths into the metamaterial, the maximum bandwidth of the transmission window reached up to 1.23 THz by changing the graphene width, and we realized the adjustable spectral extinction ratio from 26% to 98% in the modulation process. Compared with previous works, our work has superior performance in the adjustable bandwidth range. More importantly, the realization of broadening the transmission window and adjusting the spectral extinction ratio was done by manipulating the graphene instead of changing the structure of the dark and bright mode resonators. We believe that such EIT metamaterials have promising applications in broadband slow-light devices, filters, and modulators in terahertz communications.The schematic diagram of a hybrid metal–graphene terahertz metamaterial is shown in Aluminum (Al) is selected as the material for the bright and dark mode resonators, which can be described by the Drude model in the terahertz band:γ is the damping constant and In Formula (1), T is environment temperature which is set to 300 K, ω is the incident plane wave angle frequency, and ħ is the simplified Planck constant. It is worth noting that the Fermi level of graphene is represented by Γ.The conductivity of graphene is a major contributor to its properties. The conductivity of graphene is composed of the in-band and the inter-band from the Kubo equation:F ≫EBT and EF ≫Kħω, and In the terahertz region, the in-band partial conductivity can be neglected for the Pauli exclusion principle. Under this condition:  formula .(3)σg= software is used in our work. In the simulation calculation, the basic settings are as follows: the background index is set as 1.0, the environment temperature is 300 T, periodic boundary conditions are set in the x- and y-directions, and there are perfect matching layers in the z-direction. In order to reveal the physical mechanism of the transmission window in the proposed EIT metamaterials, we plot the transmitted electric field distributions of the individual VCWP and PCWP and the combination metamaterials at a 2.14 THz resonant frequency, as shown in g is less than 2.4 µm the bandwidth increases linearly and the left transmission dip shifts gradually towards the blue spectrum, but the right transmission dip is almost unchanged. Therefore, the bandwidth of the transmission window increases with the width of the graphene. For visual observation, we plotted the transmission window with various lengths g in The local schematic diagram of the structure is shown in The designed structure has superior performance in the adjustable bandwidth range, which has promising applications in broadband filters. We made the application diagram of the device as shown in F increased from 0.4 eV to 1.2 eV, the amplitude of the left and right transmission dips gradually declines. At the Fermi level of 1.2 eV, the maximum modulation depths Scon=To explain the origin behind this phenomenon, we plot the corresponding transmitted electric field |E| distributions of graphene at 0.4 eV and 1.2 eV at a 0.82 THz resonant frequency.As we can see in The extinction ratio is a significant parameter to measure the performance of optical modules. The larger the extinction ratio is, the stronger the resolution of the optical signal is, so the sensitivity is improved. However, if the extinction ratio is too large, the optical signal transmission will be easily distorted and there will be an error code at the terminal of the communication link. Therefore, it is necessary to dynamically adjust the extinction ratio according to the actual application requirements. Our group proposed an active modulation of the amplitude of the EIT-like transmission window by changing the Fermi level In summary, we propose a tunable transmission window bandwidth and spectral extinction ratio EIT-like metamaterial based on hybrid metal–graphene metamaterials in the terahertz band. It is composed of a parallel cut wire pair (PCWP) resonator, a vertical cut wire pair (VCWP) resonator, and two graphene ribbons with different widths. We plot the electrical conductivity of graphene at different Fermi levels. Through the simulation calculation, it is verified that an EIT-like transmission window with a bandwidth of 0.33 THz can be obtained through the magnetic dipole coupling between bright–dark modes. The width of the EIT transmission window can be changed by altering the lengths of both ends of horizontal graphene. The transmission window bandwidth modulation is from 0.4 to 1.23 THz. By controlling the gate voltage to change the Fermi level of graphene, the spectral extinction ratio can be adjusted from 26% to 98% and we then analyze the physical mechanism behind it, which shows that graphene with different Fermi energy levels can control the electric field energy of the device. Based on the adjustable function of the bandwidth and extinction ratio, we give a specific application and compare it with previous work. The results show that the performance and method of the device are improved. Finally, we summarize the inspiration of this work and the direction of the next work. In a way, such an EIT-like metamaterial has promising applications in broadband filters and modulators in terahertz communications.


Superconductivity and plasmonics constitute two extremely vibrant research topics, although with often nonoverlapping research communities. Here, we bridge these two active research fields by showing that graphene plasmons’ unprecedented light localization into nanometric scales can be exploited to probe the electrodynamics (including collective excitations) of superconductors. Our findings are important both from a fundamental standpoint, representing a paradigm shift , and also for future explorations interfacing nanophotonics with strongly correlated matter, which holds prospects for fostering additional concepts in emerging quantum technologies. We show that the Higgs mode of a superconductor, which is usually challenging to observe by far-field optics, can be made clearly visible using near-field optics by harnessing ultraconfined graphene plasmons. As near-field sources we investigate two examples: graphene plasmons and quantum emitters. In both cases the coupling to the Higgs mode is clearly visible. In the case of the graphene plasmons, the coupling is signaled by a clear anticrossing stemming from the interaction of graphene plasmons with the Higgs mode of the superconductor. In the case of the quantum emitters, the Higgs mode is observable through the Purcell effect. When combining the superconductor, graphene, and the quantum emitters, a number of experimental knobs become available for unveiling and studying the electrodynamics of superconductors. The superconducting state is characterized by a spontaneously broken continuous symmetry . As a co2914Naturally, the light–Higgs coupling is subjected to conservation laws, whereby translational invariance manifests in the conservation of wave vectors. Since far-field photons carry little momentum, wave vector conservation cannot be satisfied and the coupling is suppressed. However, little attention has been given to the fact that, strictly speaking, the linear-response coupling of the electromagnetic field to the Higgs mode effectively vanishes only in the →0 limit , 20. As →0 limit , 20, 21.→0 limit , 23 cons limit 23, while t limit 23–31 remaiHere, we exploit the unprecedented field confinement yielded by graphene plasmons (GPs) –2832 foFinally, we suggest an alternative observation of the GPs–Higgs coupling through the measurement of the Purcell enhancement , 33, 34 The electrodynamics of superconductors and other strongly correlated matter constitute a fertile research area , 30. In SI Appendix) (In possession of the response functions epitomized by ppendix) , 39rpsc=In what follows, we assume a typical high-4.2 meV) , 38, 41.With knowledge of the reflection coefficient for the dielectric–superconductor interface , the oveSI Appendix).Signatures of the system’s collective excitations can then be found by analyzing the poles of the corresponding reflection coefficient, which are identifiable as features in the imaginary part of the  reflection coefficient, Like ordinary conductors , supercod optics A.On the other hand, graphene plasmons not only span the terahertz regime but also attain sizable plasmon wave vectors at those frequencies , 23. Mor23graphene A and B.Furthermore, the use of graphene plasmons for probing the superconductor’s Higgs mode comes with the added benefit of control over the plasmon–Higgs coupling by tuning graphene’s Fermi energy electrostatically , 47–49. 47recision , 25, 32.l regime , 24, 27]One way to overcome the momentum mismatch and investigate the presence of electromagnetic surface modes is to place a quantum emitter , 51–53 (51m thick) , 32 or um thick) , 55]. Alm thick) . The modl effect , 33, 34.rinciple –59.Since in the near-field region the overall LDOS is dominated by contributions from ined via ρ(z)ρ0=1+ration d A and B. Top row) and Middle row). For weakly doped graphene and the larger Bottom row the Finally, d=13 nm , Top rowelation) . Consisting EFgr , 28. Ultn z=d−t′ 3. Finallenching) , 39.We have shown that signatures of a superconductor’s Higgs mode can be detected by exploiting ultraconfined graphene plasmons supported by a graphene sheet placed in a superconductor’s proximity. In particular, the presence of the Higgs mode for Experimentally, the GP–Higgs interaction can be investigated using state-of-the-art cryogenic scanning near-field optical microscopy (SNOM) . Alternaα-RuCl3 .Finally, there are a number of open questions that can spur from this work; e.g., if conductive thin films were added in direct electrical contact with the superconductor, then bound Andreev quasiparticle states inside the superconducting energy gap can form, being solutions to the Bogolubov–de Gennes equations . Anothernductors , 64, 65.The work presented here sheds light on the fundamentals of collective excitations in architectures containing two-dimensional materials and superconductors and constitutes a proof-of-principle proposal, paving the way for prospective experimental investigations into the electrodynamics of superconductors using ultraconfined graphene plasmons.Supplementary File


ATAC-seq is widely used to measure chromatin accessibility and identify open chromatin regions (OCRs). OCRs usually indicate active regulatory elements in the genome and are directly associated with the gene regulatory network. The identification of differential accessibility regions (DARs) between different biological conditions is critical in determining the differential activity of regulatory elements. Differential analysis of ATAC-seq shares many similarities with differential expression analysis of RNA-seq data. However, the distribution of ATAC-seq signal intensity is different from that of RNA-seq data, and higher sensitivity is required for DARs identification. Many different tools can be used to perform differential analysis of ATAC-seq data, but a comprehensive comparison and benchmarking of these methods is still lacking. Here, we used simulated datasets to systematically measure the sensitivity and specificity of six different methods. We further discussed the statistical and signal density cut-offs in the differential analysis of ATAC-seq by applying them to real data. Batch effects are very common in high-throughput sequencing experiments. We illustrated that batch-effect correction can dramatically improve sensitivity in the differential analysis of ATAC-seq data. Finally, we developed a user-friendly package, BeCorrect, to perform batch effect correction and visualization of corrected ATAC-seq signals in a genome browser. Active regulatory elements must remain in an accessible state to allow the binding of different transcription factors to activate or silence target genes. ATAC-seq (assay for transposase-accessible chromatin followed by sequencing) is a recently developed technique to measure genome-wide chromatin accessibility (or open chromatin)7. Compared with other techniques, such as DNase-seq, Mnase-seq, and FAIRE-seq, ATAC-seq experiments are relatively easier to perform across different tissues and cell types. Furthermore, ATAC-seq experiments allow ultra-low input cell numbers, even down to the single-cell level8. These advantages propelled ATAC-seq to be the most widely used technology to define open chromatin by many large genomics consortiums, including ENCODE9, TCGA10, PsychENCODE11, IHEC12, and TaRGET II13.Gene regulation in the mammalian genome involves different types of regulatory elements, such as promoters, enhancers, and insulators. It was estimated that there are over two million regulatory elements in the human and mouse genomes14, can identify OCRs by evaluating local enrichment against the genomic background. After peak calling, the OCRs identified in multiple samples can be combined first, and then differential analysis can be performed by estimating the difference in reads under OCRs between two different groups. Methods developed for differentially expressed gene analysis, such as edgeR15 and DESeq216, are widely used in the differential analysis of ATAC-seq data because the general assumptions in the differential analysis of ATAC-seq data are similar to those in RNA-seq analysis21: (1). Most OCRs are the same between the two conditions, and only a small portion of OCRs are significantly different and can be identified. (2). The distribution of reads under OCRs follows a certain distribution, i.e., a negative binomial distribution. However, a comprehensive comparison of these widely used tools designed for gene expression analysis is needed to evaluate their sensitivity and specificity in the differential analysis of ATAC-seq data and thereby provide users with guidelines on method choice.The peak-calling analysis used to identify open chromatin regions (OCRs) by using ATAC-seq is generally adapted from ChIP-seq data analysis. However, there are fundamental differences between ATAC-seq and ChIP-seq - most notably that ATAC-seq is performed without control or input samples. Nonetheless, peak callers, such as macs219, DESeq2 16, edgeR15, and limma18) to that of two classic statistical methods (Wilcoxon rank-sum test and Student’s t-test). We constructed a simulated dataset on the basis of the signal distribution of real ATAC-seq data and established a common set of benchmarks to evaluate the sensitivity and specificity of the six methods in different conditions. We assessed the performance of each method with different sample sizes and sequencing depths. We further compared the performance of DESeq2 and edgeR, the two most popular software programs, using real ATAC-seq data and explored the identification of DARs with different p-value and fold-change cut-offs. We further showed that batch effect correction can greatly improve the sensitivity of ATAC-seq data analysis by removing unwanted variations and can help to identify the DARs with biological meaning. Finally, we introduced our package, BeCorrect, which can correct the batch effect of ATAC-seq signal density for visualization purposes. It is available at https://github.com/Zhang-lab/BeCorrect.In this study, we compared the performance of four widely used software packages . We first designed the comparison groups with 10%, 20%, 50%, and 100% mean differences in signal density at 1, 5, and 10 CPM, which represent the low, medium, and high-signal OCRs, to evaluate the sensitivity  of all the methods. We further defined the control group (0% mean difference) at 1, 5, and 10 CPM to estimate the false positive rate. To standardize the comparisons, these six methods were only used to calculate the raw p-value, and Benjamini and Hochberg (BH) multiple testing corrections were performed to generate the adjusted p-value.In this study, we evaluated the performance of four widely used packages  for conducting differential analysis of chromatin accessibility. Meanwhile, we compared these four RNA-seq packages to two classic statistical tests (Wilcoxon rank-sum test and Student’s t-test) . All these results suggested that batch effect correction by removing unwanted variation could greatly improve the sensitivity of ATAC-seq differential analysis.We further used another published ATAC-seq data of human healthy cartilage and Osteoarthritis cartilage lot Fig. . We onlylot Fig. . Genes alot Fig. . All the34 for a deeper exploration of the correlation to target genes or other epigenomic data. Although remove unwanted variation (RUV) can efficiently correct batch effects in the differential analysis of ATAC-seq data, the signal of ATAC-seq data  cannot be directly processed by the RUVseq package. To visualize the corrected ATAC-seq signal data in the genome browser after removing the batch effects, we specifically developed a package called Bedgraph Corrector (BeCorrect) [Fig. Methods, Supplementary Fig. Most epigenomic data, including ATAC-seq, can be usually visualized on a genome browser ct) Fig. . BeCorreATAC-seq technology has been widely used to investigate the chromatin accessibility of regulatory elements and their functional roles in different biomedical research fields. The success and reliability of these studies depends on the accuracy of the identified differentially accessible regions (DARs). Here, we assessed the performance of six statistical methods and software packages by using both simulated and real ATAC-seq data. Our benchmarking study was designed to evaluate the performance of each method using the same dataset and statistical standard.We first noticed that the signal distribution of ATAC-seq data was distinct from that of RNA-seq data. This difference presented certain challenges to current widely used methods that were initially designed for the differential analysis of RNA-seq data, such as DESeq/DESeq2, edgeR, and limma. Our study based on simulated data indicated that high sensitivity was important to identify DARs with low signals, especially for distal enhancers. In our test, all RNA-seq-based software had better performance than the simple Student’s t-test and Wilcoxon rank-sum test. Interestingly, limma had the highest sensitivity in most of our studies. In particular, limma had superior performance when the ATAC-seq signal was low (1 CPM), with a small mean difference (20% and 50%) between the control and treated groups in comparison. Among all the methods, DESeq/DESeq2 had the lowest false positive rate. AUC calculations for ROC analysis suggested that all the software packages tested had very strong performance for ATAC-seq signals of 5 CPM or higher. For very low ATAC-seq signal, limma had superior performance to the other methods. All these results suggested that specific methods should be chosen based on the purpose of the DAR analysis. For users who want to minimize false positive rate, we recommend using DESeq/DESeq2, whereas for users who wish to capture the highest amount of true signal, we recommend using either limma or edgeR. For users particularly concerned with detecting OCRs in low signal regions, we recommend using limma.16. We also noticed that sequencing depth can affect the performance of differential analysis. As reported previously 30, the percentage of effective reads under peaks in real ATAC-seq data is determined by many factors, and is an important QC criterion to measure the quality of ATAC-seq experiments. To interpret these results from simulation testing, we suggest that three replicates should be the minimal requirement for an accurate ATAC-seq differential analysis. For each dataset, 20 M effective reads under peaks could ensure analysis with high sensitivity.We also evaluated the performance of different methods as a function of different sample sizes and sequencing depths. Improved performance can be clearly observed with increased number of replicates. In our study with two replicates, we noticed that edgeR had the best sensitivity with high ATAC-seq signals (10 CPM) with a 50% mean difference, which usually indicates promoter regions. However, when the sample size reached three replicates per condition, limma achieved the highest sensitivity among all the methods. DESeq and DESeq2 showed better control of false positives than limma and edgeR; however, the overall sensitivity of these two methods was lower. In particular, DESeq2 faced difficulty when applied to DAR analysis for low-ATAC-seq-signal groups. When the sample size reached seven, the sensitivity of DESeq2 in the low-ATAC-seq-signal group (1 CPM) decreased sharply, while a high performance was maintained in the high-ATAC-seq-signal group (5 CPM and 10 CPM). This serious defect in handling low ATAC-seq signal by DESeq2 might be caused by the modified regression model of log dispersions, which is different from the previous version (DESeq)We also used real data to explore the performance of DESeq2, limma, and edgeR, the three most widely used packages for RNA-seq differential analysis. All the methods had comparable performance and high consistency with each other when six replicates were included. At decreased sample sizes, edgeR showed higher sensitivity, but with a trade-off of higher false positive rate. We further used corresponding RNA-seq data to validate the DAR results by edgeR at different fold-changes and FDR cut-offs, and we concluded that a 50% of fold-change cut-off with a stringent FDR (0.01 or less) could be recommended for DARs analysis.https://github.com/Zhang-lab/BeCorrect.We also reported that batch effect correction using the RUVSeq function RUVr can dramatically improve the sensitivity of DAR analysis when real ATAC-seq data was used and was more effective at correcting batch effects than adding batches as covariant in edgeR or DESeq2. We developed BeCorrect to generate corrected ATAC-seq signal tracks for consistent visualization in an epigenome browser. BeCorrect can be applied to other epigenomic data, including ChIP-seq and DamID-seq data. The open source code of BeCorrect is available at To summarize our results, we marked top scoring methods based on their performance, including specificity Figs. , 2B overhttps://github.com/Zhang-lab/ATAC-seq_QC_analysis)30. Briefly, AIAP consists of four steps: data processing, quality control, integrative analysis and data visualization. (1) Data processing: paired-end raw reads are trimmed by cutadapt 35, aligned to the reference genome by bwa 36, and the resulting alignment BAM-format files are processed by methylQA 36 in the ATAC mode. (2) Quality control: AIAP performs multiple steps of quality checking before and after read alignment, such as reports of sequencing quality, duplication rate, GC bias by fastQC before alignment, and mapping statistics summary, chromosome distribution of uniquely mapped reads, peak width distribution after alignment. (3) Data visualization: It generates a collection of data visualization files that can be applied to a genome browser 34, including bigwig-format normalized signal density files and Tn5 insertion position files, bed-format peak files and footprint position files. It also generates JSON-format QC report files that can also be visualized via an embedded qATACviewer30. The generated peak files were merged using the merge function of bedtools suite, and the counts on each peak for Omni-ATAC replicate 1 were quantified using bedtools coverage. RNA-seq libraries for GM12878 were downloaded from SRA (SRX2370562). Raw reads were quality trimmed using cutadapt and aligned using STAR37, 2.5.4b to human genome (hg38 with gencode V27 GTF annotation). Alignments were quantified using featureCounts 1.6.4 38, and CPM were calculated using the CPM function in edgeR15. Genes with expression under 1 CPM were excluded. ATAC peak counts were scaled to CPM, and peaks under 1 CPM were excluded from analysis. ATAC-seq peak density and RNA-seq expression density were plotted in R using ggplot2. Fractions of peaks and genes with expression between 1-5 CPM, 5-10 CPM, and >10 CPM were calculated.ATAC-seq sequence files for the GM12878 cell line for Fast-ATAC and Omni-ATAC samples were downloaded from the Sequence Read Archive (SRA). QC and downstream analysis on libraries was performed using AIAP (https://github.com/Zhang-lab/ATACseq_benchmarking).To construct the simulated dataset that is similar to the real reads, we first calculated the CPM distribution within the ranges of 1-5 CPM, 5–10 CPM, and>10 CPM respectively in the mouse forebrain ENCODE ATAC-seq dataset from prenatal day 11.5 through day 16.5 and postnatal day 0 [Supplementary Fig. 2-transformed using the edgeR function “cpm” with the option ‘log = TRUE’. Additionally, each peak file was analysed by four widely used R packages for differential expression analysis (DEA) – Limma, edgeR, DESeq, and DESeq2. All comparisons were made using the default conditions for each package and function called with the single exception that the DESeq function “estimateDispersions” was called with the option fitType = “local”. Each peak file was analysed by each package or statistical comparison using all 20 replicates or by down-sampling to 15, 12, 10, 8, 6, 5, 4, 3, or 2 replicates. In all simulations, p-value adjustment was performed using the BH method with an adjusted p-value < 0.05 cut-off for significance to determine the number of each type of simulated peak called as differentially accessible unless otherwise specified. Scripts used to perform the analysis are available at https://github.com/Zhang-lab/ATACseq_benchmarking.Each peak file was analysed by two statistical tests – the two-tailed Student’s t-test and the two-sided Wilcoxon rank sum test using R. For the student’s t-test, raw counts were CPM scaled and logThe false positive rate (FPR) was defined according to the equationRecall was defined according to the equation39, DLFC24, and human cartilage33 were downloaded from GEO, and the QC check, data processing, and peak calling were performed by using AIAP 30. Peaks identified in all samples were merged together if they had ≥1 bp overlap to generate common open chromatin regions (OCRs). The read counts of OCRs in each sample were calculated by bedtools. DARs between the liver and kidney were identified by DESeq216 and edgeR15, respectively, with adjusted p-values (DESeq2) or FDR (edgeR) cut-offs of 0.01 and fold change (FC) of 100%. Down-sampling was performed by randomly excluding one replicate every time from six replicates on each side until only two replicates remained, and DAR analysis was further performed with DESeq2 or edgeR.ATAC-seq data of mouse liver and kidney39 as the ATAC-seq data. Data were aligned to mouse genome mm10 assembly by using STAR37, and the expression of genes was calculated by using featureCounts from the subread package40. ATAC-seq peaks were further assigned to the nearest genes based on GENCODE annotation (vM15). Genes contained within tissue-specific DARs at promoter regions (2 kb regions around TSSs) were selected, and their expression fold changes were compared and calculated after TMM normalization with the edgeR package. The fold-changes of the ATAC-seq signal and RNA-seq expression were plotted in R using ggplot2 in R. GO enrichment analysis were performed by using ToppGene41.RNA-seq data of mouse liver and kidney were downloaded from the same studyLibrary size normalization and dispersion estimate calculations on raw peak count files with multiple replicates of two conditions were performed using the R package edgeR. Removal of unwanted variations (RUV) correction was performed by following the manual of the RUVSeq package. Residuals were first calculated using RUVSeq in a general linear regression model, and the removal of unwanted variations by residuals was performed using the RUVr function to produce peak files with adjusted counts. DESeq2 and edgeR were also both used to perform batch effect correction by including batch as the first covariate in the experimental design.dadj is the adjusted bedgraph density, draw is the original bedgraph density, Cadj is the adjusted peak count of the first peak in a chromosome, and Craw is the raw peak count of the first peak in a chromosome.For bedgraph densities occurring from the start of a chromosome to the first peak in a chromosome, densities were adjusted according to the formulaadj and Craw represent the counts of the last peak in a chromosome from the peak count files.Bedgraph densities occurring from the last peak in a chromosome to the end of the chromosome were adjusted according to Eq.  with theadj and Craw represent the counts of the peak where the densities are located.For bedgraph densities occurring within a peak from the peak count files, densities were adjusted according to Eq.  with theFor densities occurring between two peaks in a chromosome, densities were adjusted according to the formulad is the location of the bedgraph density, L0 is the location of the end of the peak before Ld, L1 is the location of the start of the peak after Ld, C0adj and C0raw are the adjusted and raw counts of the peak before Ld, and C1adj and C1raw are the adjusted and raw counts of the peak after Ld, respectively. BeCorrect can also accept a batch effect corrected CPM table and raw CPM table instead of count tables.A batch-corrected peak count file and raw peak count file were used as input files to adjust the bedgraph densities based on four different criteria:42.Adult male Sprague-Dawley rats   were pair-housed under a 12 h light/dark cycle in a temperature-controlled (20–22 °C) and humidity-controlled room. Food and water were available ad libitum. The animals were allowed to acclimatize for one week before the start of the study. All animal procedures were conducted between 7:00 A.M. and 7:00 P.M. in strict accordance with the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee (IACUC) at Wayne State University . The description of animal procedures meets the ARRIVE recommended guidelines described by The National Centre for the Replacement, Refinement and Reduction of Animals in Research Rat brains were removed and dissected out into discrete brain areas, including the dentate gyrus and Ammon’s horn. Briefly, each brain was cut coronally into 2 mm-thick slices using rat brain matrix. The dentate gyrus and Ammon’s horn (CA1 and CA3) was punched out from slices containing dorsal hippocampus at 3.6 to 5.6 mm posterior of Bregma, using fine tip 1.5 mm surgical puncher. The tissue punches were immediately frozen and stored at −80 °C until the analysis.7 was slightly modified. A frozen tissue fragment of approximately 5–10 mg was placed into a pre-chilled 2 ml Dounce homogenizer containing 0.5 ml of cold 1x homogenization buffer 2, 10 mM Tris pH 7.8, 1x protease inhibitors, and 167 µM β-mercaptoethanol, in water). Tissue was homogenized with approximately 10 strokes with the larger “A” pestle, followed by 20 strokes with the narrower “B” pestle. Residual debris was removed by centrifugation for 1 minute at 100 RCF. Avoiding pelleted debris, 400 µl was transferred to a pre-chilled 2 ml round bottom Lo-Bind Eppendorf tube. An equal volume (400 µl) of a 50% iodixanol solution (50% iodixanol in 1x homogenization buffer) was added and mixed by pipetting to obtain a final concentration of 25% iodixanol. Then, 600 µl of a 29% iodixanol solution (29% iodixanol in 1x homogenization buffer containing 480 mM sucrose) was layered underneath the 25% iodixanol mixture. A clearly defined interface should be visible. In a similar fashion, 600 µl of a 35% iodixanol solution (35% iodixanol in 1x homogenization containing 480 mM sucrose) was layered underneath the 29% iodixanol solution. Again, a clearly defined interface should be visible between all three layers. In a swinging bucket centrifuge, nuclei were centrifuged for 20 minutes at 4800 RCF. After centrifugation, nuclei are present at the interface of the 29% and 35% iodixanol solutions. This band of nuclei was collected in a 300 µl volume and transferred to a prechilled tube containing 1 ml of ATAC-seq RSB  with 0.1% Tween-20. The nuclei were pelleted by centrifugation at 500 RCF for 10 minutes in a pre-chilled 4 °C fixed-angle centrifuge. The supernatant was removed using two pipetting steps, in which the supernatant was aspirated with a p1000 pipette first, and the remaining 100 µl was carefully aspirated with a p200 pipette. Then, 20 µl of 2x TD buffer  was added to the nuclear pellet and mixed by pipetting up and down 6 times. Nuclei were counted after the addition of trypan blue, which stains all nuclei. Then, 50,000 counted nuclei were transferred to a tube with 2x TD buffer filled in up to 25 µl. Then, 25 µl of transposition mix , 16.5 µl PBS, 0.5 µl 1% digitonin, 0.5 µl 10% Tween-20, 5 µl H2O) was added to the nuclei in 25 µl 2x TD buffer. Transposition reactions were mixed and incubated at 37 °C for 30 minutes with simple tapping every 10 minutes. Reactions were cleaned with Zymo DNA Clean and Concentrator 5 columns. The ATAC-seq library preparation was performed as described previously6. The ATAC-seq libraries were sequenced on an illuminated NextSeq platform with the 75 bp paired-end mode. The raw fastq of ATAC-seq data were processed by AIAP as described above, and differential analysis was performed by using edgeR. ATAC-seq peaks were further assigned to the nearest genes based on rat RefGene annotation downloaded from UCSC. GO enrichment analysis were performed by using ToppGene 41.The Omni-ATAC protocol for nuclei isolated from frozen tissue Supplementary Figures S1-7, Tables S1-2.Supplementary Table S3.


Diagnostic performance and transcript correlation with functional/clinical data were also analyzed. Results: 1059 differentially expressed transcripts were identified. We confirmed the downregulation of FOXF1 adjacent non-coding developmental regulatory RNA (FENDRR) lncRNA, hsa_circ_0001924 circularRNA, utrophin (UTRN) and Y-box binding protein 3 (YBX3) mRNAs. The two analyzed non-coding RNAs correlated with Forced Vital Capacity (FVC)% and Diffusing Capacity of the Lung for carbon monoxide (DLCO)% functional data, while coding RNAs correlated with smock exposure. All analyzed transcripts showed excellent performance in IPF identification with Area Under the Curve values above 0.87; the most outstanding one was YBX3: AUROC 0.944, CI 95% = 0.895–0.992, sensitivity = 90%, specificity = 88.9%, p-value = 1.02 × 10−13. Conclusions: This study has identified specific transcript signatures in IPF suggesting that validated transcripts and microarray data could be useful for the potential future identification of RNA molecules as non-invasive biomarkers for IPF.Background: Idiopathic Pulmonary Fibrosis (IPF) is a chronic degenerative disease with a median survival of 2–5 years after diagnosis. Therefore, IPF patient identification represents an important and challenging clinical issue. Current research is still searching for novel reliable non-invasive biomarkers. Therefore, we explored the potential use of long non-coding RNAs (lncRNAs) and mRNAs as biomarkers for IPF. Methods: We first performed a whole transcriptome analysis using microarray ( Idiopathic Pulmonary Fibrosis (IPF) is a progressive and lethal lung disease of unknown etiology, associated with a specific radiological and/or histological pattern of usual interstitial pneumonia. The disease is marked by a severe derangement of the lung architecture, causing cough and worsening dyspnea and ultimately leading to pulmonary failure and death. Several risk factors have been described contributing to IPF pathogenesis, although the real cause of the disease remains unknown . AccordiIPF prognosis is poor; indeed, patients survive only 3–5 years from diagnosis. In addition, IPF clinical evolution is not predictable; indeed, it is slow and gradual in some cases or rapid and often dramatic in others.Current treatments may slow down the progression of the disease, although the response to antifibrotic drugs may vary from patient to patient . The diaSeveral lncRNAs have been reported to be dysregulated and involved in the regulation of pivotal molecular pathways in fibrosis diseases of the lung, kidney, liver, myocardium and peritoneum . It has In light of these considerations, the identification of dysregulated lncRNAs in body fluids such as serum or plasma may possibly help identify novel promising clinical diagnostic or prognostic biomarkers of fibrotic lung diseases and specifically of IPF. Therefore, the main aim of this study was to analyze the whole transcriptome profile of IPF subjects compared with controls, in order to identify potential novel non-invasive biomarkers able to simplify and improve IPF diagnosis, which still represents a clinical challenge.p-value < 0.05) and 128 were down-regulated . Hierarchical clustering analysis of statistically significant deregulated transcripts showed distinguishable signatures in IPF patients with respect to the controls , associated with several molecular pathways known to be involved in IPF pathogenesis, including reticulum endoplasmic stress and Unfolded Protein Response (UPR) pathways , mitochondrial stress and function , cell aging (“Senescence and Autophagy in Cancer”), cell adhesion , inflammatory pathways  cell proliferation and migration  and fibrogenesis pathways   transcripts were statistically significant (Fisher’s Exact Test athway”) .Quantitative real-time PCR was used to confirm differential expression of four selected transcripts discovered by microarray analysis. Real time PCRs were performed in a larger independent cohort of 95 subjects . Demographic characteristics  and clinical data of the validation cohort subjects are reported in 2 scaled signal values ≥5); (II) they presented an FDR p-value ≤ 0.02; (III) they had at least a five FC deregulations.We selected the following transcripts: YBX3 and UTRN coding RNAs, FENDRR lncRNA and circ_0001924 circular RNA. We chose these transcripts because: (I) they presented a high level of fluorescence microarray signals ; UTRN . Moreover, hsa_circ_0001924 and FENDRR lncRNAs had a high diagnostic performance: hsa_circ_0001924 ; FENDRR   curve analysis. All analyzed transcripts showed an excellent diagnostic performance. The most powerful transcripts were YBX3 and UTRN coding RNAs: YBX3  . p-valuen = 14: 7 IPF, 7 CTRL). We identified 1059 deregulated transcripts; 128 up-regulated and 931 down-regulated . Interestingly, computational analysis showed that these transcripts were associated with pathways consistent with IPF pathogenesis, including fibrogenesis, inflammation, senescence, adhesion, proliferation and migration.IPF is a lung disease characterized by a relentless and progressive fibrosis of lung parenchyma. Its etiology is unknown and its pathogenesis not yet completely understood. In spite of the current antifibrotic drugs, which may slow down the progression of the disease, the prognosis of IPF remains poor. In IPF, symptoms, such as cough and dyspnea, are non-specific, therefore making diagnosis and staging of the disease particularly difficult, especially in those patients lacking the typical radiological Usual Interstitial Pneumonia (UIP) pattern at High-Resolution Computed Tomography (HRCT). In these patients, who represent more than 50% of cases, lung biopsy may represent a diagnostic solution, although not always indicated because of advanced age and poor lung function of most IPF patients. In addition, lung biopsy may induce acute exacerbations of the disease in some cases. All these diagnostic limitations make the need for an earlier, non-invasive and confident compelling diagnosis biomarker . Severalp-values four different transcripts including non-coding RNAs (FENDRR lncRNA and hsa _circ_0001924 circular RNA) and coding RNAs (YBX3 and UTRN) were selected for subsequent qPCR validation. They all were found to be downregulated in IPF patients compared with controls , with a very strong statistical significance .Based on the level of deregulation and statistical FDR These sets of RNAs may constitute new non-invasive biomarkers for the IPF pathophysiological state; indeed, in our study, the four analyzed transcripts had considerable diagnostic power for the discrimination of IPF patients with respect to the controls. Consistently, the relative ROC curves showed remarkably encouraging results for all the four analyzed transcripts with AUC values ranging from 0.865 to 0.944, sensitivity values varying from 72.5% to 90% and specificity values ranging from 88.9% to 95.6%.In addition to transcript outstanding diagnostic performances, the significant correlation between the expression levels of FENDRR and hsa_circ_0001924 with the respiratory functional parameters FVC% and DLCO% should be highlighted, suggesting their potential prognostic value, though their deregulation will be have to be validated in longitudinal cohorts. Cigarette “pack-years”, a well-known IPF risk factor, was instead correlated with the expression levels of YBX3 and UTRN.Moreover, it is important to report that a direct association between FENDRR lncRNA expression and IPF pathogenesis was observed in previous studies. In more detail, it has been demonstrated that FENDRR is downregulated in IPF, and its downregulation contributes to increased fibroblast activity and pulmonary fibrosis ,17. As fOur data could provide the basis for the identification of lncRNAs and mRNAs as classes of biomarkers allowing for an early diagnosis of IPF and contributing to improve the diagnostic accuracy of clinical data combined with imaging. However, these data should be confirmed in a larger independent external cohort. Serum RNAs should be studied in animal models in vivo, so that factors able to influence transcriptome, such as genetic and environmental factors, could be minimized. The prognostic utility of the identified markers should be evaluated through longitudinal studies, comparing the difference of expression of these molecules in a serial manner and in relation to the variation of other functional and non-functional parameters, in order to establish whether they can be useful as staging markers for IPF, alone or in association with other variables currently considered in clinical practice. The longitudinal study should also evaluate the expression of biomarkers before and after treatment with antifibrotics to evaluate the response to drugs as part of treatment management. Finally, it could be interesting to analyze whether observed transcript serum deregulation is also reflected in biopsies of IPF patients and in cellular models to establish a potential involvement of these transcripts in IPF pathogenetic pathways. This involvement should also be validated by functional experiments of transient induction or inhibition in vitro. Even with these limitations, this study offers a new and promising approach to the urgent need for biological markers able to make IPF clinical management easier, as well as paving the way for emerging concepts implementing the understanding of IPF pathophysiology.The study cohort included 52 healthy controls and 57 patients with IPF, the first were enrolled from the Internal Medicine Unit of the Garibaldi-Nesima Hospital, University of Catania, Italy and the second were recruited in the Centre for Interstitial and Rare Lung Diseases at the University of Catania, Italy. In accordance with the American Thoracic Society/European Respiratory Society ATS/ERS criteria for the diagnosis of IPF, clinical, physiologic, and high-resolution computed tomography data of these patients supported the diagnosis of IPF. Blood samples were collected from IPF patients at the time of diagnosis and retrospectively grouped.The whole cohort included a discovering cohort of 7 healthy controls and 7 IPF patients and a validation cohort of 45 healthy controls and 50 IPF patients; in both cases, controls were matched for age, gender and cigarette smoke exposure.Anthropometric indexes were collected for all the study participants; respiratory function data  were collected for IPF patients. Individuals affected by cancer, rheumatologic diseases or other lung diseases were excluded from our study.All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of ethics committee Catania 2 .g for 10 min at RT. The upper-layer supernatant was collected and aliquots were stored at −80 until analysis.Blood samples were collected in Vacutainer  tubes and serum was isolated from whole blood through centrifugation at 1500× Total RNA was isolated from 400µL of serum by using the miRNeasy mini kit according to the manufacturer’s instructions. RNA concentration and quantification were performed as previously reported ,23.The high-throughput profiling of coding/non-coding RNAs in serum samples of seven biopsy-proven IPF patients and seven matched healthy controls was performed by using Clariom D Pico Assay  technology according to the manufacturer’s instructions .p-values applying a two-sided Fisher’s Exact Test .To elucidate the function of statistically significant deregulated transcripts in IPF patients versus controls, pathway enrichment analysis was performed using Transcriptome Analysis Console v.4 , which can extrapolate canonical biological pathways from the WikiPathways  database and to return n = 95: 50 IPF, 45 CTRL). qPCR analysis was performed by using the Power SYBR Green RNA-to-CT1-Step Kit  in QuantStudio 5 Real-time PCR System . Through this kit, in a single reaction we performed both RTs and qPCRs using 20 ng of total RNA as reaction input and according to the following thermal cycle: 48 °C for 30 min (RT), 95 °C for 10 min (Activation of AmpliTaq Gold DNA Polymerase), followed by 40 cycles of 95 °C for 15 s (denaturation) and 60 °C for 1 min . Expression fold changes were calculated by applying the 2−ΔΔCt method, using GAPDH as a reference gene and healthy control subjects as a calibrator group [https://www.ncbi.nlm.nih.gov/tools/primer-blast/). For circular RNA, we used divergent primers complementary to the back-splicing junction identified through CircInteractome (https://circinteractome.nia.nih.gov/). Primer sequences are reported in In order to validate microarray results, the expression of four selected transcripts was analyzed by single real-time PCR assays in serum samples of the validation cohort  Software v.4  according to the following parameters: Analysis Type: Expression Gene, Summarization Method: Gene Level—RMA, FDR-corrected p-value < 0.05.Microarray data normalization and DE coding and non-coding RNA identification were performed by using In order to evaluate whether clinical and expression data were normally or not normally distributed, we applied three tests: D’Agostino-Pearson omnibus test, Shapiro–Wilk normality test and Kolmogorov–Smirnov normality test. Data were considered normally distributed if the results were concordant for all three tests. Unpaired t-test or Mann–Whitney U-test was used to analyze the statistical significance of real-time expression results for parametric or no parametric data, respectively.We performed linear regression analysis or Spearman correlation  in order to identify a potential relationship between transcript expression values and subject functional/clinical data. The statistical analyses were performed by using GraphPad prism 6.0 .ROC curves and AUC were used to assess the diagnostic performance of identified differentially expressed RNAs for discrimination of IPF patients with respect to the controls. SPSS PASW Statistics was used for ROC curve analysis .


BackgroundProcalcitonin (PCT) is a potential biomarker for sepsis and acts as a guide to antibiotic administration. Previous studies showed that lung cancer (LC) may increase serum PCT levels. However, no studies addressed serum PCT in patients with combined LC and idiopathic pulmonary fibrosis (IPF): LC-IPF. We aimed to evaluate the significance of serum PCT in patients with LC-IPF.MethodsA total of 137 patients with IPF who had complete follow-up data were reviewed. They were categorized into two groups: 30 patients with LC and IPF (LC-IPF) and 82 patients with IPF only (IPF). PCT assays in the two groups were done using the enzyme-linked immunosorbent assay (ELISA) technique.ResultsMedian serum PCT (IQR) was significantly higher in patients with LC-IPF in comparison to those with IPF only , respectively. LC-IPF patients with neuroendocrine (NE) component, stage IV disease, and with >2 metastatic sites had a significantly higher PCT in comparison to those with non-NE, stages I-III, and <2 metastatic sites, respectively. The presence of the NE component was the only independent risk factor predictive for PCT positivity in patients with LC-IPF; OR1.8  0.042-2.145; p = 0.042).ConclusionPatients with LC-IPF have higher serum PCT levels than those with IPF alone. These levels are related to the presence of NE component, advanced cancer stage, and the presence of multiple metastases. The presence of the NE component is the only independent risk factor predictive for PCT positivity in patients with LC-IPF. Further studies are warranted. Idiopathic pulmonary fibrosis (IPF) is the most common of the idiopathic interstitial pneumonias and carries the worst prognosis . DespiteStudy design and populationAssiut University Hospital (AUH) and South Egypt Cancer Institute (SECI) are two large tertiary hospitals serving a large number of populations at Upper Egypt. A systematic search of the patient database at the chest department of AUH revealed that 152 patients fulfilled the international guidelines on the diagnosis and management of IPF [1) The first group included IPF patients with newly diagnosed LC who had (at the time of diagnosis of IPF) or developed LC (biopsy-proven) during their follow-up period; it was named the LC-IPF group. Once diagnosed with LC, the patients were referred to the department of medical oncology at SECI for further oncologic management. Among this group, the time of the PCT assay was at LC diagnosis, and patients with the following conditions were excluded: acute exacerbation of IPF (AE-IPF), history of inflammatory disease that may modify PCT levels, clinical suspicion , or laboratory signs of bacterial or viral infection, fever of unknown origin, initiation of chemotherapy, radiation therapy, or any history of either, comorbidities that could affect the serum levels of PCT, and the use of anti-inflammatory drugs or systemic steroids. (2) The second group included those patients with IPF only (IPF group). Also, among this group, patients with the following conditions were excluded: AE-IPF, history of inflammatory disease that may modify PCT levels, clinical suspicion or laboratory signs of bacterial or viral infection, fever of unknown origin, comorbidities that could affect serum levels of PCT, and the use of anti-inflammatory drugs or systemic steroids. Then, the two groups were compared for clinical features and serum PCT levels. Figure  The firsData collectionThe medical records of all patients were reviewed to obtain data regarding age, smoking history, the method used to diagnose IPF, time of IPF diagnosis, PFT, comorbidities if any, follow-up duration, and outcomes. Pack/year value was calculated by multiplying the number of packs of cigarettes smoked per day by the number of years the person has smoked . An acutPCT and C-reactive protein (CRP) measurementsAll serum PCT measurements were performed in the medical physiology laboratory, blinded to the clinical data. Two mL venous blood was collected once from every patient in the two groups. Sera were separated and stored at -20°C till the time of analysis. Serum PCT was measured quantitatively by the enzyme-linked immunosorbent assay (ELISA) technique using the RayBio® Human PCT ELISA kit  according to the manufacturer's protocol. Values were expressed in nanograms per milliliters (ng/ml). The test was considered positive when >0.10 ng/ml. Clinically relevant PCT cut-off values were determined by using those reported in published decision-making algorithms: 0.25 and 0.5 ng/ml -19. SeruStatistical analysisComparing the distribution of non-normally distributed variables was done using the Mann-Whitney U test in the case of two samples and the Kruskal-Wallis test for more than two samples. To identify risk factors associated with PCT positivity, a binary logistic regression model was used. If variables showed a significant difference in the univariate analysis, they were entered into a multivariate model. The odds ratio was given with the respective 95% confidence interval (CI). All tests were two-sided and a p-value of <0.05 was considered significant. Statistical analyses were carried out using the Statistical Package for the Social Sciences (SPSS) software, Version 22.0 .Patients’ characteristicsOut of 137 patients with IPF who were followed up, 38 had the diagnosis of LC-IPF. So, the prevalence of LC in IPF patients was 38/137 (27.7%). The number of patients who did not fulfill the criteria for PCT measurement was eight and 17 among patients with LC-IPF and those with IPF only, respectively. PCT was successfully measured in 30 and 82 patients with LC-IPF and IPF only, respectively, and constituted the two study groups ) serum PCT was 3.58 ± 7.08 and 0.28 ± 0.52 ng/ml in patients with LC-IPF and IPF only, respectively, while median serum PCT (IQR) was 0.655± 3.60 and 0.07 ± 0.11ng/ml, in the two groups, p=0.016, respectively. Mean (SD) serum CRP was 46.98 ± 21.19 and 14.19 ± 8.21 mg/dL, in patients with LC-IPF and IPF only, while median serum CRP (IQR) was 33.20 ± 8.78 and 13.15 ± 7.59 MG/dL, in the two groups, p=0.002, respectively  were diagnosed as having primary LC at the same time as the IPF diagnosis. The other 22 (73.3%) patients developed LC 18.6 ± 15.8 months  after the diagnosis of IPF during the follow-up period. Squamous cell carcinoma  and adenocarcinoma (40%) were the most common histological findings in LC-IPF patients. Four (13.3%) patients had large cell carcinoma (LCC), with a neuroendocrine component in three of them. Two (6.7%) patients had small cell carcinoma. Four (13.3%) patients had early stage I lung cancer. Five (16.7%), nine (30%), and 12 (40%) patients had stage II, III, and IV, respectively. Twelve (40%) patients had metastases. Table 6.7% wereSerum PCT levels and patients with LC-IPFIn relation to the demographic data of patients with LC-IPF, there were significant differences between the serum levels of PCT with regards to histopathologic type, tumor stage, and the number of metastatic sites while there were no significant differences with regards to gender and CRP levels. LC-IPF patients with a neuroendocrine component, those with stage IV disease, and with more than two metastatic sites had significantly higher serum PCT levels in comparison to those with a non-endocrine component, stages I-III disease, and less than two metastatic sites, p = 0.001, p=0.014, and p=0.004, respectively. The sites of metastases included the liver, bone, and central nervous system (CNS) (10 each), lung (2), pleura (2), and adrenal glands (1) Table .Risk of serum PCT positivity in patients with LC-IPF Among analyzed demographic and tumor characteristic variables, the univariate analysis showed that the presence of a neuroendocrine component is the only independent risk factor predictive for PCT positivity among patients with LC-IPF. Patients with neuroendocrine tumors had the odds ratio of 1.8 , in comparison to those with a non-neuroendocrine component. Table To the best of our knowledge, this is the first study that addresses the value of serum procalcitonin in stable patients with combined lung cancer and idiopathic pulmonary fibrosis. In comparison to stable patients with IPF alone, patients with LC-IPF had significantly higher PCT levels (p=0.016). LC-IPF patients with a neuroendocrine component, with advanced-stage cancer, and with more than two metastatic sites had significantly higher serum PCT levels. The presence of a neuroendocrine component is the only independent risk factor predictive for PCT positivity.This is the first study that compares the serum levels of the commonly used marker procalcitonin between patients with IPF and those with combined LC-IPF. Previous reports had addressed levels of PCT either in acute exacerbations of interstitial lung disease  or LC alOur study observed that stable patients with LC-IPF had significantly higher PCT levels than stable patients with IPF alone. This difference is closely related to the development of LC in patients with IPF, as the majority (73.3%) of IPF patients developed LC 18.6 ± 15.8 months after the initial diagnosis of their IPF. Our finding is in agreement with many reports that observed that PCT can be elevated due to malignancy -14,21-22In our cohort of LC-IPF patients, those with a neuroendocrine component, advanced-stage disease, and with more than two metastatic sites had significantly higher serum PCT levels in comparison to those with a non-endocrine component, stages I-III disease, and less than two metastatic sites, respectively. These findings are similar to, and supported by, the findings of previous reports. PCT is secreted ubiquitously by neuroendocrine cells located in the lung, adrenal, liver, kidney, adipose tissue, and muscles in response to sepsis -25. CateDespite the small number of our enrolled patients, the current study might have significant clinical implications. During regular follow-up of patients with IPF, the presence of abnormally high serum PCT in the absence of known triggers  should alert the physician to the possible presence of LC . In dailThe findings of the current study indicate that patients with combined IPF and lung cancer have higher serum procalcitonin levels than those with IPF alone. These high levels were related to the presence of a neuroendocrine component, advanced cancer stage, and the presence of multiple metastases. The presence of a neuroendocrine component is the only independent risk factor predictive for PCT positivity among patients with LC-IPF. These high PCT levels should be taken into consideration upon follow-up of IPF patients for the development of LC, as well as upon the management of infections and/or sepsis in patients with LC-IPF. Further studies are warranted.


A significant (p < 0.001) increase in sPD-L1 levels was detected in patients after the first cycle and during therapy vs. baseline. A longer overall survival (OS) was observed in patients with sPD-L1 concentrations below ) or FC values above  their statistically calculated optimal cut-offs. On the basis of these initial results, the specific role of CTLA-4-, PD-L1-, or PD-1-targeting on sPD-L1 release was then investigated in sera from 81 additional ICI-treated solid cancer patients. Results showed a significant (p < 0.001) increase of sPD-L1 levels during therapy compared to baseline only in anti-PD-L1-treated patients, supporting the specific involvement of PD-L1 targeting in the release of its soluble form. Our findings suggest that sPD-L1 represents a predictive biomarker of clinical response to anti-PD-L1 cancer immunotherapy.Targeting of the programmed cell death protein (PD)-1/programmed death-ligand 1 (PD-L1) axis has shown a significant clinical impact in several tumor types. Accordingly, our phase II NIBIT-MESO-1 study demonstrated an improved clinical efficacy in mesothelioma patients treated with the anti-PD-L1 durvalumab combined with the anti-cytotoxic T-lymphocyte antigen (CTLA)-4 tremelimumab, as compared to tremelimumab alone. Due to the promising therapeutic activity of immune check-point inhibitors (ICIs) in mesothelioma patients, the identification of biomarkers predictive of response to treatment is of crucial relevance. The prognostic role of soluble PD-L1 (sPD-L1) proposed in cancer patients prompted us to investigate this protein in sera from mesothelioma patients ( The therapeutic relevance of monoclonal antibodies (mAbs) targeting inhibitory immune checkpoint(s) has been validated in a variety of tumor types . Over thPromising results are emerging by the combined targeting of different immune checkpoint inhibitors. In particular the NIBIT-MESO-1 study was the first to demonstrate encouraging signs of efficacy from treatment with tremelimumab combined with durvalumab in first or second-line malignant mesothelioma patients . SubsequHowever, clinical efficacy of anti-PD-1/PD-L1 seems to depend on tumor intrinsic characteristics such as the neoantigen load and the tumor mutational burden ,17; hencPossible specific mechanisms of regulation carried out by sPD-L1 in relation to different ICIs treatments, as well as their potential clinical implications, need deeper insights. To this end, in this study we exploited the availability of serum samples from an homogeneous cohort of mesothelioma patients, enrolled in the NIBIT-MESO-1 trial (NCT02588131), to investigate the presence and levels of sPD-L1, before and in the course of treatment with the anti-CTLA-4 mAb tremelimumab combined with the anti-PD-L1 mAb durvalumab. Results were broadened by analyzing the effect of anti-CTLA-4, anti-PD-1, and anti-PD-L1 treatment on serum levels of sPD-L1 in a cohort of 81 patients with tumors of different histotypes. Levels of sPD-L1 were correlated with clinical parameters to define their predictive role in ICI therapy.p < 0.001) difference in the mean values of sPD-L1 was observed between mesothelioma patients 0.07 ng/mL (range from 0.01 to 0.15 ng/mL) and healthy donors . To investigate kinetic changes in sPD-L1 levels, sera of NIBIT-MESO-1 patients were analyzed before drug infusion at day 1 of cycle 2 (d1C2), C3, and C5 in the course of treatment, and levels of sPD-L1 were compared to those detected at baseline. Already at d1C2, all patients showed a statistically significant (p < 0.001) increase in the sPD-L1 levels, with respect to baseline, that was maintained in the course of treatment with median values of sPD-L1 concentration and fold change vs. baseline (FC) at each investigated time-point ranging from 1.52 ng/mL (d1C2) to 1.76 ng/mL (d1C5), and from 22.71 (d1C2) to 27.28 (d1C3), respectively   analyses were utilized to identify the optimal sPD-L1 concentration and FC value cut-offs able to stratify patients according to their OS . Results= 0.004) . No asso= 0.004) .p = 0.018) at d1C2, 32.75 vs. 13.14 months (p = 0.006) at d1C3, and 27.35 vs. 12.86 months (p = 0.016) at d1C5 was observed for patients with sPD-L1 FC values higher than the best cut-offs identified by ROC curves ; (ii) neutrophil-to-lymphocyte ratio (NLR) ; and (iii) C-reactive protein (CRP) concentrations   (data not shown). In the course of treatment, a significant negative correlation was identified between sPD-L1 and the absolute eosinophil count (AEC) at d1C2  and d1C5   . A signi = 0.02) . Finally, no significant correlation was observed between sPD-L1 levels and overall response rate (ORR); immune-related ORR; disease-control rate (DCR); immune-related DCR; or patients age, gender, or clinical stage at all investigated time points.Under the assumption that the increase of sPD-L1 levels in NIBIT-MESO-1 patients could be related to treatment, and to discriminate the role of specific ICIs in this phenomenon, levels of the soluble protein were evaluated in serum of additional solid cancer patients (#49), treated with anti-PD-L1 or anti-CTLA-4 in monotherapy. Detection of sPD-L1 was performed at baseline and at specific time-points of treatment.p < 0.01) increase of sPD-L1 levels was observed in the course of treatment with anti-PD-L1 mAb, in all investigated patients with median values of sPD-L1 FC increasing at each investigated time-point vs. baseline, ranging from 18.25 at d1C4 to 43.9 at d1C3  treated with anti-PD-1 mAbs. Results showed that median values of sPD-L1 FC, in these patients, did not significantly change between baseline and the following administration cycles, ranging from 1.05 at d1C3 and d1C4 to 1.12 at d1C2 .p < 0.001) increment in sPD-L1 concentration after the first cycle of treatment  that was maintained through the following cycles of administration reaching the highest median value (1.78 ng/mL) at d1C3. Consistently, median values of sPD-L1 FC increased at each investigated time-point vs. baseline, ranging from 19.77 at d1C4 to 28.58 at d1C3   . On the  = 0.01) .p < 0.01 and p = 0.04, respectively)  .Baseline levels of sPD-L1 were correlated with different clinical parameters. The Spearman’s rank correlation was performed to investigate a causal relationship between sPD-L1 and age, gender, stage, eastern cooperative oncology group performance status (ECOG PS), and the overall response rate without observing a statistical significance.p < 0.001) higher sPD-L1 concentration in mesothelioma patients compared to healthy donors. Considering the immune suppressive role of PD-L1, these data represent evidence of the predominant immunosuppressive contexture detected in cancer patients compared to healthy donors. Of note, levels of circulating sPD-L1 seem to be heterogeneous in cancer patients; specifically, our results showed that levels of sPD-L1 detected in mesothelioma patients were lower compared to those reported by other studies in cancer patients of different tumor histotypes, such as gastric cancer, B-cell lymphoma, and multiple myeloma [The status of PD-L1 expression on tumor tissue from cancer patients has been the most studied and used as a predictor of efficacy for PD-1/PD-L1 blockade therapy. Although the results collected from different clinical trials ,33,34,35 myeloma ,38,39. FTo date, limited information is available in order to describe the mechanism(s) of sPD-L1 release, and it is still unclear whether levels of sPD-L1 may have a predictive role in the course of ICI therapy. To this end, in this study, we investigated this molecule in a cohort of mesothelioma patients treated with the anti-CTLA-4 mAb tremelimumab combined with the anti-PD-L1 mAb durvalumab, within the NIBIT-MESO-1 trial NCT02588131). Supporting the possible role of this soluble protein as a predictive biomarker of response to ICI therapy, an inverse correlation between concentrations of sPD-L1 and a longer OS was observed at baseline. This observation is consistent with results previously described in other tumor histotypes. Indeed, low levels of sPD-L1 at baseline correlated with a better clinical benefit of NSCLC patients treated with anti-PD-1 mAb  and with1. Suppor2R = 0.937, p = 0.007) and CD8+  T-cells in NIBIT-MESO-1 patients , suggest that immune cell-surface expression of PD-L1 could be a source of the sPD-L1 detected in our cohort of patients. Moreover, the anti-PD-L1 treatment, inducing an activation of immune response, could increase the release of IFN-γ by effector cells rising the levels of sPD-L1 in patients’ sera [Moreover, considering the evolution of the soluble protein, during ICI therapy, a significant increase of sPD-L1 levels was observed in patients immediately after the first cycle. It is noteworthy to report that sPD-L1 FC vs. baseline, already measured after the first cycle of therapy, were found to be significantly associated to a longer OS of mesothelioma patients, providing fundamental support to the role of changes in circulating sPD-L1 as a non-invasive biomarker for an early prediction of response to immunotherapy. Of note, the direct association observed between patients with a FC over the best cut-off at each time-point and the significant longer survival was strictly dependent upon the baseline level of sPD-L1 of these patients, as confirmed by the significant correlation found between higher FC of sPD-L1 during ICI therapy and the lower baseline concentration. The correlation between constitutive levels of circulating sPD-L1, their FC during anti-PD-L1 therapy, and OS of cancer patients remains to be fully clarified. Along this line, the activation status of immune system induced by ICI therapy could be involved in the shedding of sPD-L1, contributing to the high FC of sPD-L1 released in patients with a longer OS. Although the exact origin of sPD-L1 in cancer patients remains unclear, significant correlations, observed between increase of circulating sPD-L1 levels and fall of PD-L1 expression on both CD4+ . Additional studies aiming to clearly define this topic in a larger cohort of patients should be performed.A number of 121 patients diagnosed with tumors of different histotypes and treated with anti-PD-L1, anti-PD-1, or anti-CTLA-4 mAbs were investigated . They ing for 15 min at 4 °C (within 30 min after collection), aliquoted, and subsequently stored at −80 °C. Haematologic blood tests  and centrifuged at 1600× od tests  were perLevels of sPD-L1 in sera from patients were measured by the enzyme-linked immunosorbent assay (ELISA), using the PD-L1/B7-H1 Quantikine ELISA Immunoassay kit , according to the manufacturer’s protocol. The optical density of each well was measured with the Benchmark PLUS Microplate spectrophotometer  at 450 nm with wavelength correction set at 570 nm. sPD-L1 concentrations were extrapolated from a specific six-point standard curve. Results were reported as sPD-L1 concentrations or as FC values. The FC values were calculated as the ratio of sPD-L1 concentrations at each time-point compared to the baseline.p-value < 0.05 was considered statistically significant. Statistical analyses were carried out by GraphPad Prism 7.05 .Results were analyzed by descriptive statistics to determine median and concentration ranges of sPD-L1. Changes in sPD-L1 levels between baseline and different time-points during treatment were investigated by the Mann–Whitney U test. Correlations between sPD-L1 levels and the patients’ clinical status, as well as biochemical and peripheral blood count parameters , were coIn conclusion, we demonstrated that both low baseline levels of sPD-L1 and their high FC increase during therapy significantly correlated with a longer OS of mesothelioma patients enrolled in the NIBIT-MESO-1 study. Moreover, we provided the first piece of evidence that increased shedding of sPD-L1 specifically associates with anti-PD-L1 therapy in cancer patients. Comprehensively, these results warrant further investigation to corroborate the use of sPD-L1 as a predictive biomarker of response to anti-PD-L1 therapy in cancer patients.