Background: Lineage 1 (L1) and 3 (L3) are two lineages of the Mycobacterium tuberculosis complex (MTBC) causing tuberculosis (TB) in humans. L1 and L3 are prevalent around the rim of the Indian Ocean, the region that accounts for most of the world’s new TB cases. Despite their relevance for this region, L1 and L3 remain understudied.Methods: We analyzed 2,938 L1 and 2,030 L3 whole genome sequences originating from 69 countries. We reconstructed the evolutionary history of these two lineages and identified genes under positive selection.Results: We found a strongly asymmetric pattern of migration from South Asia toward neighboring regions, highlighting the historical role of South Asia in the dispersion of L1 and L3. Moreover, we found that several genes were under positive selection, including genes involved in virulence and resistance to antibiotics. For L1 we identified signatures of local adaptation at theesxH locus, a gene coding for a secreted effector that targets the human endosomal sorting complex, and is included in several vaccine candidates.Conclusions: Our study highlights the importance of genetic diversity in the MTBC, and sheds new light on two of the most important MTBC lineages affecting humans. The global tuberculosis (TB) epidemic is a major public health emergency, disproportionately affecting vulnerable populations and worsening existing inequalities. Although the estimated incidence and the number of fatalities have slowly decreased over time, every year ten million people develop the disease, and 1.4 million TB patients die . MoreoveMycobacterium tuberculosis complex (MTBC), which includes nine phylogenetic lineages with different geographic distributions. Five of these lineages are restricted to Africa , while the remaining four  are more broadly distributed .We screened a large collection of publicly available MTBC genome sequences and selected those belonging to L1 and L3. Additionally, we newly sequenced 767 strains to cover regions that were not well represented in the public data. We applied a series of bioinformatic filters to exclude low quality sequences and mixed infections (Methods), and obtained a curated genome dataset of 4,968 strains . While the phylogenetic tree of L3 showed a ladder-like topology without an evident division in sublineages, L1 comprised five clearly distinct sublineages coalescing close to the root of the tree . Because we were interested in studying L1 and L3 in their endemic range, we excluded strains originating from North America, Europe, and Australia from the biogeographic analysis, as in these low-burden regions, most TB patients are recent migrants who were infected in their country of origin .The genome sequences included in our final dataset represented 69 countries and covered the known geographic range of L1 and L3 . Most interestingly, we found a strongly asymmetric pattern of migration. For L1, PASTML identified most migration events from South Asia toward other regions, followed by further dispersion, but almost no back migration to South Asia . When we estimated the migration rates with Mascot, we found that the forward migration rate from South Asia toward the rest of the world were 3 to 17 times larger than the migration rates in the opposite direction, confirming the results of PASTML (Extended data: Table 2). We found a similar scenario for L3: PASTML inferred the largest number of migrations from South Asia toward East Africa, further spread from East Africa toward neighboring regions, but essentially no migration back to South Asia (Extended data: Files 3–4). The Mascot analysis showed that the forward migration rates from South Asia toward East Africa were 26 times larger than the migration rates in the opposite direction (Extended data: Table 2).We performed a phylogeographic analysis using two alternative approaches (Mascot and PASTML), which are based on different models and inference methods . However, the results of a previous study suggested a relatively fast evolutionary rate for L1 . By contrast, L3 had a good temporal signal, and different methods estimated that its MRCA lived between the 2nd and the 13th century AD. However, the uncertainty around all of these estimates was very large . Together, these results corroborate the findings of previous studies .With respect to the geographical aspects, we identified several interesting instances of potential long-range dispersal. First, we found a clade of L1, composed of 11 strains sampled in five different West African countries. This was surprising because West Africa is usually not considered part of the geographic range of L1. This clade is nested within sublineage L1.1.1, which is essentially only found in mainland Southeast Asia, and the PASTML analysis inferred a direct introduction from Southeast Asia to West Africa. This introduction is unlikely to have happened before the 16Extended data: Files 1 and 2). Assuming a clock rate of 1.4×10-7, the earliest introduction was between 1620 and 1830 AD from East Africa, while subsequent introductions occurred from East Africa, South Africa, and South Asia. These results support the hypothesis that L1 was first introduced to Brazil through the slave trade from East Africa . This would be compatible with early Portuguese expeditions, which reached East Timor and Papua New Guinea in the early 16th century. An alternative explanation could be the Austronesian expansion. Austronesians are thought to have reached the Comoros islands and Madagascar between the 9th and the 13th century AD, possibly through direct navigation from Southeast Asian islands. Malagasy speak an Austronesian language, and Austronesian genetic signatures are found in human populations in the Comoros, Madagascar, and to a small extent also in the Horn of Africa . However, this difference can be explained by the different size and diversity of the two datasets . The epitope that accumulated most mutations was located at the N terminus ofesxH (Rv0288). This peptide is a T cell epitope for both classes, MHCI and MHCII; it is also a B cell epitope and was previously identified as one of the few T cell epitopes that were not hyper-conserved (Extended data: Table 7). Interestingly, we did not find this signature in L3, as all strains carried the ancestral haplotype at the N-terminalesxH epitope. Moreover, when we extended the analysis to two large genomic datasets of L2 and L4 strains, we found a much weaker signal: while 21% of L1 strains carried a derived haplotype for this epitope, only 1% of L2 and L4 strains, and no L3 strain had a derived haplotype. Despite analyzing datasets with more strains , and more polymorphic positions  compared to L1, we found only three amino acid replacements at the N-terminal epitope ofesxH in L2, and seven in L4  in at least one L1 strain, while only 20% were variable in at least one L3 strain  but not in the other lineages . PAML identified four codons that have been under positive selection in L1 (posterior probability > 0.95), all of them within the N-terminal epitope  amino acid replacements occurred in sublineages L1.1.1 and L1.2.1, which are almost exclusively present in Southeast Asia . We constructed a statistical test of association similar to phyC (esxH epitope were significantly associated with a particular geographic region (Methods). We found that South African strains were less likely to harbor a derived haplotype in the N-terminalesxH epitope than expected by chance  with different HLA-A, HLA-B and HLA-DRB1 alleles (Methods). We performed this analysis for:We hypothesized that the accumulation of missense mutations at the N-terminal epitope of1) Alleles found at high frequency (> 10%) in South- and Southeast Asia, but not in East Africa.2) Alleles found at high frequency (> 10%) in East Africa, but not in South- and Southeast Asia.3) Alleles found at high frequency (> 10%) in both regions.Extended data: Table 8).However, we found no differences in the predicted binding affinities between alleles with different geographic distributions . We found two genes coding for transmembrane proteins, members of the Esx-1 secretion system, which were under positive selection in L1 , and several genes involved in antibiotic resistance that were under positive selection in both lineages (Extended data: Information). We further characterized the profile of drug resistance mutations of L1 and L3, and found that L1 strains harbored a greater proportion ofinhA promoter mutations (conferring resistance to isoniazid) compared to L3 strains, confirming previous findings . WhileOur results highlight the central role of South Asia in the dispersion of L1 and L3. First, we confirmed that the two lineages probably expanded from South Asia . As we already mentioned, tip-dating analyses of MTBC trees with roots that are several hundreds of years old is extremely challenging, and the results of such analyses should be taken with caution . It is known that L1 strains interact differently with the infected hosts compared to other lineages. For example, L1 strains show a lower virulence in animal models . Region-specific signatures of positive selection are a hallmark of local adaptation; in this case, adaptation of L1 strains to human hosts with South- and Southeast Asian genotypes. This corroborates previous studies reporting that in Taiwan, L1 is associated with indigenous populations with Austronesian ancestry (reviewed inesxH after Southeast Asia (islands), 37.3% and 72.8% respectively, opposed to 0.3% (one single strain) in the rest of East Africa.The signatures of selection at the N-terminal epitope ofesxH were contained in one single T cell epitope, the selective pressure acting onesxH could be due to some other factor, and not to the binding of the epitope by the MHC, or to its recognition by T cells. A possible mechanism was suggested by experiments in a mouse model, showing that the N-terminal epitope of EsxH generates an immunodominant CD8 T cells response. The amino acid replacement A10T  did not change MHC binding or T cell recognition, but it accelerated the degradation of the epitope, disrupting the immunodominant response , impairing trafficking, and hindering phagosomal maturation and antigen presentation, thus contributing to the survival of the pathogen , in MTBC strains belonging to L2 . Because of these limitations, our biogeographical analyses were limited to the subcontinental level. This approach revealed the global population structure and the main macroscopic patterns of diversity and migration of L1 and L3. However, MTBC populations are diverse also within subcontinental regions. For example, the MTBC population in Southern India is dominated by L1, while the most prevalent lineage in the North is L3  of MTBC strains belonging to L1 and L3, using the diagnostic SNPs described inhttps://github.com/jstjohn/SeqPrep). The resulting reads were mapped to the reconstructed MTBC ancestral sequence (https://github.com/broadinstitute/picard). The RealignerTargetCreator and IndelRealigner modules of GATK v.3.4.0  .We applied the following filters: genomes were excluded if they had 1) an average coverage <15x, 2) more than 50% of their SNPs excluded due to the strand bias filter, 3) more than 50%  of their SNPs having a percentage of reads supporting the call between 10% and 90%, 4) contained single nucleotide polymorphisms diagnostic for different MTBC lineages  with raxml 8.2.11 . For both lineages, we built SNP alignments including only variable positions with less than 10% of missing data. We inferred phylogenetic trees with RAxML 8.2.11 , using tWe used the least square method implemented in LSD v0.3-beta  random subsampling 2) random subsampling keeping at least 25 genomes for each year of sampling . For this second subsampling strategy, we used Treemmer v0.3  prior instead of the exponential growth prior, and performed a nested sampling analysis .For the biogeography analysis, we considered only genome sequences obtained from strains for which the locality of sampling was known. When the country of sampling did not correspond to the country of origin of the patient (or was unknown), we considered as sampling locality the country of origin of the patient . Furthermore, similarly to other studies . We inferred phylogenetic trees with raxml 8.2.11 , using t-7 for L1, and 9×10-8 for L3. In this analysis, genomes for which the sampling date was not known were assumed to have been sampled between 1995 and 2018, which is the period in which all strains with known date of isolation were sampled. Importantly, using different clock rates for this analysis would only change the time scale of the trees, but not the reconstruction of the ancestral characters.Since PASTML needs a time tree as input, we calibrated the phylogenies with LSD, assuming a clock rate of 1.4×10We assigned to each strain the subcontinental geographic region of origin as character, and used PASTML  and 9×10-8 (L3). We assigned the tip sampling years to the different strains, and when the sampling time was unknown, we assumed a uniform prior from 1995 to 2018 . We further assumed the best fitting nucleotide substitution model as identified by jModelTest, a gamma prior for the standard deviation of the lognormal distribution of the clock rate [0 –infinity], and a lognormal prior for the population size with standard deviation = 0.2, and mean estimated in real space. Finally, we used an exponential distribution with mean = 10-4 as prior for the migration rates. For each analysis, we ran at least two runs. We used Tracer 1.7.1  an average coverage <15x, 2) more than 50%  of their SNPs having a percentage of reads supporting the call between 10% and 90%, or 3) contained single nucleotide polymorphisms diagnostic for different MTBC lineages (Extended data: Table 1). We reconstructed the phylogenetic tree of L2 with raxml 8.2.11 . We considered separately MHCI epitopes and MHCII epitopes, and we analyzed overlapping epitopes individually. We mapped the epitope sequences onto the H37Rv genome (GCF_000195955.2) using tblastn and excluded sequences mapping equally well to multiple loci in the H37Rv genome. Additionally, we retained only epitopes that mapped with two mismatches or less over the whole epitope length. This resulted in a final list of 539 MHCI epitopes, and 1,144 MHCII epitopes. (Extended data: Table 7). We used the datasets obtained for the biogeography analysis .We downloaded the amino acid sequence of all MTBC epitopes described forFor each lineage, we independently assembled a multiple sequence alignment for each epitope. We then translated the sequence to amino acids and used PAUP 4.a  to reconesxH (MSQIMYNYPAMLGHAGDM), which was by far the epitope with most amino acid replacements in L1. We expanded the analysis of this epitope to the L2 and L4 datasets, so that we could compare the results with L1 and L3. For the PAML analysis, we randomly selected 500 strains from each MTBC lineage. We used the phylogenetic tree reconstructed by RAxML (same settings as above), and the gene alignment to estimate the branch lengths of the tree using the M0 codon model implemented in PAML 4.9e , modeling nearly neutral evolution; M2a assumes one additional ω (ω2 > 1) compared to M1a, thus modeling positive selection. We performed a likelihood ratio test between the two models as described inExtended data. The codon under positive selection were identified with the Bayes empirical Bayes method  to PhyC  Alleles that had a frequency of 10% or more in at least one population in South- and Southeast Asia, but had frequencies lower than 10% in all East African populations.2) Alleles that had a frequency of 10% or more in at least one population in East Africa, but had frequencies lower than 10% in all populations in South- and Southeast Asia.3) Alleles that had a frequency of 10% or more in at least one population both in South- and Southeast Asia and in East Africa.in silico binding prediction with three epitopes: the ancestral epitope at theesxH N terminus (MSQIMYNYPAMLGHAGDM), and the two most frequently observed derived epitopes . For HLA-A and HLA-B alleles, we used the NetMHCPan4.1 server (https://www.iedb.org/).For all these alleles, we performedesxH. We considered as under positive selection all genes, for which the likelihood ratio test resulted in a Bonferroni-corrected p-value < 0.05.For this analysis, we used the subsets generated for the Mascot analysis. These datasets are representative of the populations of L1 and L3 in their core geographic ranges, and are computationally treatable. We generated sequence alignments for all genes individually, excluding genes in repetitive regions of the genome (see above). Because some genes are deleted in L1 but not in L3, or the other way around, we obtained a slightly different number of gene alignments for the two lineages . For each gene, we performed a test for positive selection with PAML as described above forWe considered 196 mutations conferring resistance to different antibiotics , and conhttps://www.ncbi.nlm.nih.gov/sra) under the accession number PRJNA670836.The sequence data generated by this study has been deposited on SRA . As far as I can tell, this calibration is the major driving force for the difference in L1 TMRCA from O'Neill. It would be interesting to know how the degree to which this calibration affects the TMRCA and clock rate. On a somewhat related note, this article fails to cite some key references on the phylogeography of TB. In one section the authors evaluate association between esxH haplotypes and the geographic region of origin. They calculate this association using a home-made statistical test based on phyC. I could not gather from the description exactly what they measure. It is a simulation-based test that measures "how many strains from each region resulted to have a derived state according to the simulation". Is there a test statistic being ranked here? Is it single-region vs the rest or is the statistic a composite measure of multiple regions? How is the simulation for the null distribution set up? E.g. is it a forward-in-time simulation using a specified mutation rate or a permutation or something else? I see that the code for running this test is posted on github - However, I still think a few more details in the actual paper is in order for the biologists that don't read R. Speaking of this test, It is a bit unconvential for Table 1 to highlight all results with P<0.1, especially since a large number of associations are being tested here. I would not consider p<0.1 to be particularly strong evidence of association. I'd like to commend the authors for making so much of their data publicly available. Datasets are in immediately-usable formats which makes it simple to download all the raw data, and as mentioned code is also readily available. HLA abbreviation never explained in manuscript.Tip date randomisation performed, but the results of this test is never stated.Why is PRJNA630228 posted? As far as I can tell it doesnt contain any SRA data, and the other accession contains data for all 767 strains. Very minor:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Bioinformatics, microbial genomicsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you very much for your comments, and for reviewing our manuscript. We address all your points below. We prepared a revised version of the manuscript based on the feedback of all reviewers.Regarding the clock analysis of L1:et al. 2019) where we analyzed a different L1 dataset and found a good temporal signal and a clock rate of ~1.4x10-7. So far Menardoet al. 2019 is the only published study that estimated the clock rate for a L1 dataset. We performed a tip dating analysis but found no temporal signal for L1. Therefore we used the clock rate estimate resulted from a previous study  were compatible with the hypothesis of a West African introduction of L1.1.1 posterior to the Portuguese circumnavigation of Africa. We found that only the largest clock rate was compatible with this hypothesis. In a second step we tested whether this clock rate and two alternative values, 5x10et al. 2019: “This discrepancy is due to different assumptions about the clock rates of L1, but none of the two hypotheses can be excluded with the available data”. We are quite explicit in the manuscript about the limitation of this analysis, and how it should be interpreted in comparison to the results of O’Neill Finally, it is possible that the West African L1.1.1 clade did not originate through a direct migration from South East Asia. However, a series of short range migrations seems less likely, as we did not find strains belonging to L1.1.1 in South and Central Asia, or in East Africa. As we write in the manuscript, the dating analyses for MTB in general, and for this L1 dataset in particular, have limitations. Here we discussed what we consider the most likely scenario given the data, we did not exclude alternative scenarios. We added a section in the discussion comparing our findings with the biogeography of other MTBC lineages, and referencing the relevant studies. We revised and expanded the Methods section regarding the phylogenetic test of association to provide more details. To answer the specific questions: the statistic ranked is the number of tips with a derived state; the test is performed for each region individually; the simulation does not use a mutation rate, but it distributes randomly a fixed number of mutations on the tree (the same number inferred from the observed data). We also removed special fonts from Table 1, now all p-values have standard formatting.Minor points: We now write HLA in full at the first occurrence. The results of the DRT are reported in the supplementary Information (now amended to enhance clarity), in Sup. Figs 8 and 9 and in Sup. Table 3. In the main text we refer more generally to the lack of temporal signal , as this reflects the results of the DRT, but also of the Beast analyses. We now refer only to PRJNA670836, which contains data for all the newly sequenced strains. Thanks. Mycobacterium tuberculosis of Lineages 1 and 3. Historically, and while forming an important part of theM. tuberculosis species diversity, the study of both lineages have been neglected in comparison to Lineages 2 and 4. The authors of this study assembled a large genomic dataset encompassing 2938 and 2030 L1 and L3 isolates, respectively, and by implementing distinct molecular evolutionary algorithms were able to put forward a scenario for the dissemination of these strains. A South Asian origin is proposed for both clades from where these strains appeared to have radiated via migration-driven dispersion towards East Africa (L3) and other regions (L1). The weakest aspect of the work is the lack of phylogeographic and historical context of L1 and L3 versus L2 and L4 dissemination, especially L4 in South-America and L2 in Asia in the context of the migratory waves.This study depicts a very interesting and relevant account on the evolutionary history and phylogeography ofesxH derived haplotypes were not randomly distributed through the geographic range of L1 and the finding of positive selection acting on vaccine candidates, such as this, is important and deserves attention. The authors used PASTML and Mascot to conduct the phylogeographic analyses, revealing the migratory pattern of these lineages from South Asia revealing the origins of these two lineages. Several genes involved in virulence and drug resistance were shown to be under positive selection in L1 and L3 strains with important adaptative consequences. Importantly,th century that impacted the dissemination of L1 in Africa and other regions. The manuscript is very well written, easy to read and understand and the methods are robust and well applied and described. Important to note the absence of a few important references to other groups work on the third wave migration of migrations in the 16Some comments that can be useful for discussion: The analysis pertaining the introduction of L1 to South America is based on a more modest number of isolates (n=77), all but one from Brazil. This limits the robusteness of the conclusions for this region. It is possible that the evolutionary history of L1 in South America is far more complex than the one outlined in the article and there may be missing links. For example, EAI (L1) strains have been detected in several South and Central American countries  and besides slave trade, the Spain-operated Manila-Acapulco Galleon trade route (est. ca. 1568) connected Southeast Asia to Central and South America. Regarding the L1.1.1 sub-lineage, what precludes these strains to have been introduced to West Africa in a more recent period other than the XVI or the XVII century? As far as we can tell, this pertains a group of 10 isolates scattered over five countries, but what is the degree of divergence between these isolates? Eventually, can this also be linked with the indentured labor promoted by the British Empire in the XIX century? On the epitope analysis, upon downloading epitopes from IEDB did the authors include only epitopes with positive assays and was there a threshold for including only epitopes validated by independent assays (more reliable)? Also, how did overlapping epitopes been handled?Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Mycobacteriology, Tuberculosis, Laboratory Diagnosis and Molecular Epidemiology.We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you very much for reviewing and commenting our manuscript. We prepared a revised version of the manuscript based on the feedback of all reviewers. Regarding the comparison with other lineages: in this manuscript we focused on L1 and L3 because they are understudied. We now revised the text and added a section comparing our findings with the biogeography of other MTBC lineages, and referencing the relevant studies. Additionally, it is true that the evolutionary history of L1 in South America could be more complex, and we might fail to capture it because we have samples from a single country (Brazil). We now explicitly write this.th century is the earliest possible time for the introduction of L1 in West Africa (assuming that the migration could not have happened before the circumnavigation of Africa by Portuguese), we do not exclude the possibility of a later introduction. However, this group of 11 L1 strains comprising the West African clade has a relatively old MRCA. To be compatible with an introduction in the 19th century, the molecular clock rate would need to be about 3x10-7 . This would be a fast rate for MTB , and we find this hypothesis unlikely. Nevertheless, because of the lack of a temporal signal we cannot exclude a more recent introduction of L1 in West Africa. Regarding the L1.1.1 sub-lineage: in the manuscript we write that the 16 Finally, in the epitope analysis we downloaded all T cell epitopes, to be as inclusive as possible. Overlapping epitopes were analyzed independently. We added this information to the manuscript. Mycobacterium tuberculosis complex (MTBC) strains belonging to Lineages 1 and 3, which are the most prevalent lineages in South Asian countries with a high TB burden, such as India. The authors compiled an excellent dataset of nearly 5,000 MTBC genomes from these two lineages, comprising publicly available as well as newly-sequenced MTBC strains. Their phylogenomic analyses showed that L1 comprises a further five distinct sublineages, whereas L3 does not contain distinct sublineages. The authors used two different methods (PASTML and Mascot) for conducting phylogeographic analyses, with results from both methods suggesting an interesting asymmetric pattern of migration: these lineages originated in and migrated from South Asia to other regions, with very little back-migration into South Asia. This underscores the important role South Asia has played in the dispersal of these lineages. The authors found several genes involved in virulence and antibiotic resistance to be under positive selection in L1 and L3 strains. Lastly, the authors also found evidence of local adaptation at the esxH epitope in L1 strains from South and Southeast Asia, which requires further study as this epitope is a part of several vaccine candidates. The aim of this study was to conduct a phylogeographic analysis ofStrain cultures, DNA extraction and genome sequencing" is missing citations for the methods used (eg. CTAB extraction method).The Methods section titled "Figures 1A and 2A heatmaps would look better in color than grayscale. The Supplementary Information would benefit from a thorough proofreading for grammatical and typological errors.  Overall, I found this to be a well-designed and thorough study, and a very well-written manuscript. I have no major concerns and only a few minor suggestions for aesthetic improvement:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Pathogen genomicsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you very much for your comments, and for reviewing our manuscript. We prepared a revised version of the manuscript based on the feedback of all reviewers. We added the missing reference for the DNA extraction method, and we revised the supplementary material for errors and typos, thanks. Regarding Figures 1A and 2A: in both figures the other three panels have the same color code, where different colors correspond to different world regions. We decided to use a gray scale to avoid potential confusion of having two different color codes in the same figure. We did not modify the figures.


Matrix metalloproteinases (MMPs) and tissue inhibitors of MMPs (TIMPs) play important roles in the turnover of extracellular matrix and in the pathogenesis of idiopathic pulmonary fibrosis (IPF). This study aimed to determine the utility of circulating MMPs and TIMPs in distinguishing patients with IPF from controls and to explore associations between MMPs/TIMPs and measures of disease severity in patients with IPF.n = 300) came from the IPF-PRO Registry, an observational multicenter registry of patients with IPF that was diagnosed or confirmed at the enrolling center in the past 6 months. Controls (n = 100) without known lung disease came from a population-based registry. Generalized linear models were used to compare circulating concentrations of MMPs 1, 2, 3, 7, 8, 9, 12, and 13 and TIMPs 1, 2, and 4 between patients with IPF and controls, and to investigate associations between circulating levels of these proteins and measures of IPF severity. Multivariable models were fit to identify the MMP/TIMPs that best distinguished patients with IPF from controls.The IPF cohort (CO) % predicted and higher composite physiologic index (worse disease). MMP9 was associated with the composite physiologic index. No MMP/TIMPs were associated with forced vital capacity % predicted.All the MMP/TIMPs analyzed were present at significantly higher levels in patients with IPF compared with controls except for TIMP2. Multivariable analyses selected MMP8, MMP9 and TIMP1 as top candidates for distinguishing patients with IPF from controls. Higher concentrations of MMP7, MMP12, MMP13 and TIMP4 were significantly associated with lower diffusion capacity of the lung for carbon monoxide (DLCirculating MMPs and TIMPs were broadly elevated among patients with IPF. Select MMP/TIMPs strongly associated with measures of disease severity. Our results identify potential MMP/TIMP targets for further development as disease-related biomarkers. Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease associated with high mortality . Two antThe pathobiology of IPF involves excess production of extracellular matrix (ECM) and dysregulated matrix remodeling . Matrix Several basic and clinical studies have underscored the importance of MMPs and TIMPs in the pathobiology of IPF, as recently reviewed . In partIdiopathic Pulmonary Fibrosis Prospective Outcomes (IPF-PRO) Registry (NCT01915511) [The IPF cohort was drawn from the multicenter observational US 1915511)  that enrThe control cohort was drawn from the Measurement to Understand the Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study, a registry of adult residents of North Carolina in which self-reported health information and biological samples are collected . To ensun = 23 IPF and n = 17 control), MMP8 (n = 48 IPF and n = 63 control), MMP12 (n = 7 IPF and n = 4 control), and MMP13 (n = 10 IPF and n = 3 control) were extrapolated if feasible or assigned a concentration of half the minimum observed value. No samples fell below the standard curve for MMPs 2, 3, 7, or 9 or TIMPs 1, 2, and 4.Enrollment plasma samples were assayed for antigenic levels of MMPs 1, 2, 3, 7, 8, 9, 12, and 13 and TIMPs 1, 2, and 4 . MMPs 2, 3, 9 and TIMP1 and MMPs 1, 7, 8, 12, and 13 were quantified using multiplexed luminex immunoassays. TIMP2 and TIMP4 were quantified by ELISA. Samples that fell below the standard curve for MMP1  and test sets  using stratified sampling to retain the 3:1 ratio in each set. Three linear models  and 4 non-linear models  were fit. Covariates for age and sex were included in each model. When fitting each model, 10-fold cross validation was used to choose the optimal tuning parameter based on the area under the receiver operating curve. Operating characteristics  were determined in the training set and then evaluated in the test set. Variable importance measures were determined for the best performing models [Multivariable analyses were performed to assess whether a set of MMPs or TIMPs could differentiate patients with IPF from controls, and log10 data were centered, scaled, and Box-Cox transformed to improve model efficiency. No MMPs or TIMPs were highly correlated (Pearson correlation coefficient of ≥0.9) so all analytes were retained in the analysis. The data from the 400-patient cohort were randomly divided into training , which correlates with the extent of fibrosis on radiography in patients with IPF [1), and the reference equations developed by Crapo and Morris [For univariate analyses in the IPF cohort, linear regression models were employed on the log10 transformed data to determine the association between circulating levels of each MMP/TIMP and three measures of disease severity, analyzed as continuous variables: FVC % predicted, DLwith IPF . The Natwith IPF . were usd Morris  were useCO % predicted, and CPI were modeled separately to examine their relationship with sets of MMPs/TIMPs. Pairwise correlation analysis indicated that no MMPs or TIMPs were highly correlated (Pearson correlation coefficient of ≥0.9) so all were retained in the analysis. The performance of 2 linear models  and 4 non-linear models  was measured on training  and test  sets. Covariates for age, sex, current use of nintedanib, and current use of pirfenidone were included in each model. While fitting each model, 10-fold cross-validation was used to choose the optimal tuning parameter in the training set, minimizing the root mean squared error (RMSE). R-squared was computed.In multivariable analyses of the IPF cohort, FVC % predicted, DLn = 100), the median  age was 66.0  years, 74% were men, 68% were former smokers (Table n = 300), the median  age was 70.0  years, 74% were men, 94% were white and 67% were former smokers  and CPI was 53.5 . The lower and upper tertile cutpoints for these measures are shown in Additional file In the control cohort n = 00, the mp-value < 0.05) in patients with IPF versus controls with the exception of TIMP2  CPI score  and MMP13 (DLCO). Specifically, the estimated differences in DLCO % predicted per 10-fold increase in circulating MMP7, TIMP4, and MMP13 were − 13.41, − 11.80%, and − 6.37%, respectively. For CPI, the estimated difference per 10-fold increase in circulating MMP7 was 11.01 points, while for TIMP4 it was 7.94 points  concentrations of each MMP/TIMP by tertile of FVC % predicted, DL concentr1% predicted.Given that prior reports suggest that circulating MMP or TIMP expression can be altered in smoking-related respiratory diseases such as chronic obstructive pulmonary disease and emphysema , 22, andAs nearly all of the MMPs and TIMPs measured were elevated in patients with IPF, we sought to understand whether sets of these proteins differentiated patients with IPF from controls better than any single protein. There was no collinearity for the MMPs and TIMPs, thus a multivariable approach may more appropriately account for synergistic or antagonistic relationships that may exist between these molecules. We found that in general, linear multivariable models  had similar or better operating characteristics compared with more complex non-linear methods in the training set, with most models obtaining classification accuracies between 80 and 90% over all iterations of the cross-validation procedure Fig. . In the Multivariable models were fit to understand whether sets of MMPs/TIMPs predicted measures of disease severity in this IPF cohort. The maximum R-squared value achieved for predicting any disease severity measure was 0.16 in the training set  CPI. This finding is of interest, as TIMP4 differs from other TIMPs in that at homeostasis, its expression is restricted to cardiovascular structures [CO or CPI and peripheral blood TIMP4 expression reflects pulmonary vascular remodeling and development of pulmonary hypertension, accumulating lung fibrosis, or the effects of angiogenesis supporting pulmonary hypertension and fibroplasia.In our IPF cohort, we observed higher levels of TIMP4 to be associated with lower DLructures . Moreoveructures . It is uConsistent with our results, a prior study implicated MMP8 as a differentiating protein in a 5-protein classifier  of IPF status, although the classification model did not select MMP9 and did not consider TIMP1 . Our resWhen our data on circulating MMP or TIMP levels in patients with IPF are considered in the context of data from experimental models of fibrosis, a clearer view emerges of the potential mechanism(s) by which altered MMP/TIMP expression contributes to lung fibrosis. Both MMP3 and MMP7 appear critical to the development of experimental lung fibrosis, with rodents genetically deficient in MMP3 or MMP7 demonstrating a reduction in pulmonary fibrosis after bleomycin challenge –37. MMP7In contrast to these pro-fibrotic MMPs, animal models suggest that augmented levels of MMP1 decrease fibroplasia in liver, muscle and heart –48, whilWhile our study has several strengths, including the multicenter nature of the IPF cohort, we acknowledge that it has inherent limitations. First, although we characterized a broad array of MMPs, we did not include all described MMPs nor TIMP3. Additionally, while our MMP assay provides precise quantification of circulating MMP concentrations, MMP activity and organ specificity cannot be inferred. Second, as pulmonary function data were not available for the majority of subjects prior to the date of enrolment, we cannot ascertain whether patients were experiencing significant disease progression or were relatively stable at the time of sampling. Finally, although this work contributes important new information regarding circulating MMP and TIMP expression in patients with IPF, our study was not designed to understand whether the observed changes are specific to IPF as compared with non-IPF fibrosing lung diseases. As the IPF-PRO Registry has recently expanded to include patients with non-IPF interstitial lung diseases in the ILD-PRO Registry, we anticipate that future studies will address the specificity of candidate biomarkers for IPF as compared with other fibrosing lung diseases.The results of this study further delineate the potential value of selected MMPs or TIMPs as disease-related biomarkers in patients with IPF. Further validation will be necessary, as will extension of these analyses to examine associations between MMP/TIMP expression and clinical outcomes. Rich longitudinal data collected in the IPF-PRO Registry, including serial pulmonary function measures, hospitalization data, and information on vital status will support these analyses and further the goal of improving the diagnosis and management of patients with IPF.Additional file 1: Tertile cutpoints for disease severity metrics.Additional file 2: MMP and TIMP concentrations (pg/mL) in patients with IPF versus the control population.Additional file 3: Median  concentration for each MMP/TIMP stratified by tertile of FVC % predicted, DLCO % predicted, and CPI.Additional file 4: Association of MMPs and TIMPs with measures of IPF severity, adjusted for anti-fibrotic treatment.Additional file 5: Mean (standard deviation) log10 MMP and TIMP concentrations in the IPF and control populations as stratified by smoking status at enrollment (current/past smoker vs. never smoker).Additional file 6: Mean (standard deviation) log10 MMP and TIMP concentrations in patients with IPF with or without clinically significant emphysema on CT scan as assessed by the enrolling physician.Additional file 7: Model performance in the training  set for baseline FVC % predicted, DLCO % predicted and CPI.Additional file 8: Model performance in the test set for baseline FVC % predicted, DLco % predicted and CPI.


The synthesis of two-dimensional (2D) transition metals has attracted growing attention for both fundamental and application-oriented investigations, such as 2D magnetism, nanoplasmonics and non-linear optics. However, the large-area synthesis of this class of materials in a single-layer form poses non-trivial difficulties. Here we present the synthesis of a large-area 2D gold layer, stabilized in between silicon carbide and monolayer graphene. We show that the 2D-Au ML is a semiconductor with the valence band maximum 50 meV below the Fermi level. The graphene and gold layers are largely non-interacting, thereby defining a class of van der Waals heterostructure. The 2D-Au bands, exhibit a 225 meV spin-orbit splitting along the  Dimensionality of a material is a critical parameter to control its electronic properties. Here, the authors report that 2D gold transforms from a semiconductor, with valence band maximum 50 meV below the Fermi level, into a metal by tuning the number of layers from 1 to 2 in between graphene and SiC. In particular, there are essentially no single-element materials other than graphite that can be easily exfoliated14. 2D metals see a vast field for applications, ranging from catalysis to sensing16, passing through enhanced magnetism20. This enticing applicative potential has prompted the development of various approaches for the synthesis of low-dimensional metals15. Several chemistry-based strategies have been implemented, however mostly yielding nanostructures of different shapes and geometries15. Physical methods have been shown to effectively yield monolayers of transition metals, typically on other metal surfaces23. The main focus of those types of investigations was the interactions between heavy atoms yielding large spin-orbit splitting of surface states, which might be useful for spintronic devices24. However, the strong interaction between the metallic substrate and the 2D layer typically hinders the possibility to access the intrinsic properties of the latter. Also, keeping the surface energy low enough to avoid the growth of multilayer islands is a non-trivial problem to address and often a capping medium solution must be adopted25.Lowering the dimensionality of a given compound has become a big hype in the field of material research, as this opens the possibility to explore and exploit properties, which are inaccessible in a bulk crystal. While several compounds can be relatively easily rendered two-dimensional (2D) with physical approaches28 or zerolayer graphene (ZLG) on SiC(0001) as well as under epitaxial graphene on Ni(111)29. The effects reported were doping of the graphene π-bands31 or mini-gaps opening in the graphene Dirac cone due to moiré superperiodicity32. It was recently shown that the interaction between the 5d orbitals of gold and the valence band of graphene can open a spin-orbit gap deep in the valence band of gold-intercalated graphene on SiC(0001)33. However, no evidence of two-dimensional dispersing bands stemming from the metal layer was ever reported, which would be indicative of a well-defined order of the film.In this article, we shed a light onto the electronic properties of two-dimensional gold (2D-Au), by synthesizing it via the intercalation of Au atoms at the heterointerface between the Si-terminated SiC(0001) surface and its C-rich  based system34 they can solely be attributed to the interfacial Au layer.Photon energy dependent ARPES measurements were performed to probe a possible crystallographic periodicity along the entclass1pt{minima31. A single gold layer acts as an electron donor to graphene, shifting the π-bands down until the Dirac point E0 is observed about 700 meV under the Fermi level, as visible from Fig. εeff = 7 ± 1, which allow us to detect the plasmaron band35. The crossing energy of the plasmaron band is indicated as E1 and further details can be found in the Supplementary Note The gold atoms at the interface affect the electronic properties of grapheneThe size and shape of the 2D-Au BZ suggest that the Au atoms are arranged on a triangular lattice with the lattice parameter matching that of SiC(0001), i.e., 3.08 Å. As a further support to this hypothesis, we have observed with ARPES, graphene replica bands shifted by a SiC(0001) reciprocal lattice vector (cf. Supplementary Note We compared the observed bands to a tight binding (TB) model calculated on this 2D triangular lattice in the next-nearest neighbor (NNN) approximation:a is the lattice parameter, γ01 and γ02 are the NN and NNN hopping parameters, the first being of the order of 1 eV and γ02≃γ01/10. For the plot of Fig. γ01 = 1.1 eV and a = 3.08 Å, that is the SiC(0001) lattice parameter.k, confirming the right choice of a. The validity of the model also confirms the 2D nature of the states. However, some small deviations are present, especially around Apparently, even this simple model describes the measured band dispersion quite well. In particular, the extremes are placed at the same 36. These simulations show the formation of Au-interface related bands, similar to the ones observed in our experiment. There, it was presumed, that the structure of the interfacial Au is arranged such that every topmost Si atom of the SiC is bound to one Au atom, i.e. a (1 × 1) with respect to the SiC(0001). Similar to our experiment, their bands have the maximum in close proximity to the Fermi level, which indicates a semiconducting behavior.Density functional theory (DFT) calculations have been published for this system by Chuang et al.37 the authors find a strong spin-splitting stemming from spin-orbit coupling. The spin texture there resembles a Rashba-type with a Rashba parameter of 1.638 eVÅ−1. In fact, we do find a strong correspondence to this in the experimental band structure , low-energy electron diffraction (LEED) and scanning tunneling microscopy (STM) measurements. Representative spectra of the Au 4f doublet indicates the presence of atoms in a single chemical environment. The binding energy that we measure is ~350 meV higher than for metallic gold and this excludes the presence of a substantial amount of metallic gold either on the surface or underneath graphene. In contrast, it indicates that Au is bound to Si at the interface, as confirmed by the Si 2p spectrum, which shows a component of bulk SiC and a surface component of Au–Si. Symmetry considerations allow us to conclude that, since the bands we observe are of s-p character, the Au atoms must interact with the 2p Si orbitals by their 5d electrons.The details about the fitting parameters are reported in Supplementary Table 38. What we observe is rather a significant electronic overlap between the topmost Si atoms of the substrate and the intercalated gold layer. The interaction with the substrate stabilizes the gold layer and induces the interfacial order. Notably, Au atoms deposited onto SiC(0001) are observed not to order on the (1 × 1)39, which implies the need for the graphene on top to impose the observed ordering and electronic structure.It should be noted that this does not represent an alloyed silicide layer, which is also reported to have a different (higher) binding energyμ-LEED pattern of Fig. The From the data reported above, we find that every Au atom is bound to a Si atom at the interface . Going from a single Au layer to a Au bilayer does not only switch the doping of graphene from 11 cm−2, which, given a band velocity of (1.33 ± 0.05)⋅106 ms−1, corresponds to a shift of the Fermi level by (−150 ± 10) meV with respect to the Dirac point. We as well observe the Rashba splitting at about −1 eV due to the interaction between the graphene and gold bands, as reported in ref. 33.Figure ky direction, with reference to Fig. 24. Similar states are as well observed for the SC phase, as visible from the ARPES data acquired at 75 and 40 eV, reported in Supplementary Figs. The interfacial gold still develops its own band dispersion, with a rich structure, as exemplary shown in Fig. entclass1pt{minima36, even if they considered three layers at the interface instead of two. Such a band structure implies that at any given small variation of the chemical potential, or applied external voltage, there will always be a finite electronic density of states, confirming the metallicity of this phase.We mark the states reaching the Fermi level with the orange dashed line and a solid circle. The spectral weight at f doublet fitted with two components. The ratio between the two is very close to one, indicating a symmetric distribution of the atoms between the first and the second layer.XPS measurements confirm the presence of a gold double layer, as does STM . This is illustrated by the μLEED, ARPES, and STM measurements. The estimation of the lattice parameter is (2.93 ± 0.01) Å, about 1.6% larger than the nominal 2.883 Å of the Au(111).Such a grid—represented in Fig. a and indicated as a yellow disc in panel c. The difference in C-Au adsorption distance emerges from the local variation of the atomic registry between the lattices.When such a gold bilayer interacts with graphene, it develops a superperiodicity half the size, which is observed in STM, as visible in the data reported in Fig. n = (3.45 ± 0.05)⋅1013 cm−2 when on SC 2D-Au, to p = (9 ± 1)⋅1011 cm−2 when on M 2D-Au. The doping difference, i.e., a shift in the Dirac point position, is about 700 meV and is reflected almost entirely to a work function difference between the two phases , we define a material with semiconductor character, exhibiting the maximum of the valence band at an average energy of 67 meV below the Fermi level. We have determined that the gold atoms arrange themselves in a highly ordered triangular lattice, mimicking the SiC(1 × 1) and develop their own band structure. Such an electronic band dispersion has a truly two-dimensional character and it is notably well reproduced by a simple TB model. Tuning the amount of intercalated gold from one to two layers, leads to a two-dimensional gold with metallic character. The interatomic forces within the gold layer make the lattice parameter shrink from 3.08 Å, to 2.93 Å, yet not quite relaxed into the Au(111) configuration, which has 2.88 Å as atomic spacing. Graphene is also affected by the underlying 2D gold in terms of electronic properties. Its doping level goes from Besides the deep fundamental interest provided by this class of materials, we envision a vast portfolio of possible applications for this system, ranging from detection of THz radiation, to more sophisticated optoelectronic devices involving the spin degree of freedom. It will moreover serve as a platform for exploring non-linear optics effects in two-dimensional gold, a field so far essentially unexplored.n-type substrates were used in the present study and were purchased from SiCrystal GmbH. Polishing damages were removed by etching the substrate at 1500 °C in a purified H2 atmosphere. The SiC(0001) surfaces were graphitized by annealing the samples in argon atmosphere following the procedure introduced by Emtsev et al. in ref. 40. The ZLG develops upon annealing in Ar atmosphere at about 1400 °C for 10 min45. The formation of the ZLG results in a carbon-rich reconstruction with periodicity  and that they do not require any particular protection from the atmospheric environment. In UHV conditions, they can be heated up to 850 °C before the intercalated gold starts to be deintercalated and desorbed from the surface.Nominally on-axis oriented single crystalline single-side polished 6H-SiC 2 of the BESSY II  synchrotron facility at a temperature of 90 K. The hemispherical electron analyzer used for the data harvesting was a Scienta R8000 with a diameter of 200 mm and an ultimate energy resolution of 1 meV. In our experiments the instrumental resolution was set to about 2.5 meV, hence smaller than the thermal broadening of the bands, i.e., about 7.5 meV. Additional ARPES measurements have been carried out at the I4 end-station of the MAXlab  synchrotron facility with a SPECS Phoibos 100 analyzer. The aforementioned lines have also been used to acquire X-ray photoelectron spectroscopy (XPS) data. Preliminary studies to clarify the preparation procedures and to assess the sample’s quality were carried out in the home-laboratory at the MPI Stuttgart using a Specs Phoibos 150 analyzer and monochromatized He II radiation for the photoexcitation. Low-energy electron microscopy (LEEM) measurements, as well as micro-ARPES (μARPES), micro-LEED (μLEED), and micro-XPS, were acquired at the beamline I311 beamline of the Maxlab using an Elmitec III microscope. More details about the performances of the microscope can be found elsewhere48. The scanning tunneling microscopy (STM) measurements were performed with an Omicron LT-STM microscope at the CNI@NEST in Pisa at a temperature of 78 K.The band structure of the system was mapped by means of ARPES at the end-station 1Supplementary Information


Accordingly, graphene and MoS2 have been widely studied to be applied in the development of biosensors. Moreover, nanohybrid materials developed by hybridization of graphene and MoS2 have a huge potential for developing various types of outstanding biosensors, like electrochemical-, optical-, or surface-enhanced Raman spectroscopy (SERS)-based biosensors. In this review, we will focus on materials such as graphene and MoS2. Next, their application will be discussed with regard to the development of highly sensitive biosensors based on graphene, MoS2, and nanohybrid materials composed of graphene and MoS2. In conclusion, this review will provide interdisciplinary knowledge about graphene/MoS2 nanohybrids to be applied to the biomedical field, particularly biosensors.Graphene has been studied a lot in different scientific fields because of its unique properties, including its superior conductivity, plasmonic property, and biocompatibility. More recently, transition metal dicharcogenide (TMD) nanomaterials, beyond graphene, have been widely researched due to their exceptional properties. Among the various TMD nanomaterials, molybdenum disulfide (MoS Ever since their unique properties were first reported, nanomaterials have been widely researched and applied in various scientific fields ,2. In ge2) are two of the most commonly studied materials being researched in recent years . T. T88]. Tty of GO . In addity of GO .In addition to electrochemical biosensors, graphene and GO have been widely employed to develop SERS-based biosensors due to the graphene-enhanced Raman scattering . For exa3+, cytochrome C (Cyt C), and phosphate (Pi), as shown in 3+ is an important ion that acts as a co-cofactor in metalloproteins, and it is also known to cause neurodegenerative diseases and organ dysfunction when it is present in excess in humans 3−/4−-containing solution with square wave voltammetry. The [Fe(CN)6]3−/4− was used as a redox probe and exhibited a current value through the electron transfer process with the surface of the HIV biosensor. When the gp120 was added and combined with the gp120 antibody on the Au/MoS2/Au multilayer, the redox signal from [Fe(CN)6]3−/4− was blocked, decreasing the measured current due to the nonconductivity of the captured gp120. The decreasing current value was linearly proportional to the added gp120 concentration, enabling the sensitive detection of gp120 concentration. Using this HIV biosensor, the gp120 was measured with a linear response in a broad concentration range of 0.1 pg/mL to 10 ng/mL. Because of the effective electron transfer and large surface area of the MoS2 NPs, it showed higher current values compared to the normal Au substrate prepared without MoS2 NPs. Based on the detection limit of 0.066 pg/mL, it was proved that gp120 was measured more sensitively than in other electrochemical HIV biosensors previously reported. One example of a MoSubstrate . To devect gp120 a. The de2 nanosheet and lactate oxidase (LOx) . . 2 nanos+) biosensor composed of a MoS2 nanosheet and FITC-attached ssDNA (FITC–ssDNA) . . 2 nanos2 QD and MnO2 nanosheet . T. T2 nanoosensors ,142,143.lication . During 2 nanohybrid, such as the 3D graphene/MoS2 aerogel and rGO decorated with MoS2 QDs . T. T2 nano 10−7 M) ,163.2 nanohybrid is being utilized to develop outstanding biosensors with high sensitivity and selectivity. In addition, there are some researchers trying to utilize the graphene/MoS2 nanohybrid to develop wearable or flexible biosensors and electronic devices [2 nanohybrid is expected to play a role as a key component in the development of next-generation biosensors. All of the studies discussed in detail in this section are summarized in As discussed in this section, the graphene/MoS devices ,165,166.2 are particularly promising nanomaterials for highly sensitive and selective biosensors. Graphene, with 2D single carbon atomic layers arranged in a honeycomb lattice structure, possesses the most exceptional properties among numerous carbon-based nanomaterials, such as high conductivity, biocompatibility, and large surface area. TMD nanomaterials have also been studied recently because of their direct bandgap, optical, semiconducting, and electrical properties. These two nanomaterials can both help achieve high sensitivity and selectivity in biosensors and are also suitable for conjugation with biomolecules because of their excellent biocompatibility. Moreover, in recent years, studies have been conducted to create biosensors with graphene/MoS2 nanohybrids to achieve synergistic effects from each nanomaterial.Since nanomaterials were first discovered, numerous nanomaterials have been synthesized, researched, and applied in many scientific fields because of their unique properties. Among the many nanomaterials, graphene and MoS2, and graphene/MoS2 nanohybrids. The discussion was organized into four categories. First, the properties of graphene and MoS2, the synergistic effects derived from the conjugation of graphene and MoS2, and their advantages for developing the biosensors were provided. Next, graphene-based biosensors were discussed that employ an excellent electron transfer facilitation, quenching, and chemical enhancement effects for developing biosensors. Then, MoS2-based biosensors were described. These biosensors utilize the unique characteristics of the MoS2 in various structures, such as its luminescence and electrochemical properties. Finally, recent biosensors based on a graphene/MoS2 nanohybrid were discussed. These biosensors incorporated various structures of the graphene/MoS2 nanohybrid, which provide the advantages of each material and induce exceptional synergetic effects. Although various types of graphene/MoS2 nanohybrid have shown potential for biosensing application, there are still issues to be solved for practical or future applications. Above all else, a uniform mass production, the commercialization of various types of graphene/MoS2 nanohybrid, and reducing the production cost are essential for conducting efficient researches. Nevertheless, it is clear that a graphene/MoS2 nanohybrid is one of the most advanced nanomaterials for next-generation biosensors. In addition, through research to find more efficient types or structures of graphene/MoS2 nanohybrids, these can be used in wearable biosensors or applied to commercially available POCT. In conclusion, the recent research covered in this review, based on graphene/MoS2 nanohybrids, provides a new approach and interdisciplinary knowledge to advance the development of highly sensitive and selective biosensors.This review discusses recently reported biosensors based on graphene, MoS


The surface morphology and electronic structure of hexagonal graphene onion rings (HGORs), a modified graphene structure, were investigated to confirm the possibility as an efficient catalyst when compared to graphene. After confirming the formation of HGORs with a smaller width (~4.2 μm) from scanning electron microscopy (SEM) and optical microscopy images, we compared the catalytic activities of HGORs and graphene by measuring the rate of oxidation of thiophenol using high-resolution photoemission spectroscopy (HRPES). In addition, we also assessed in 4-chlorophenol degradation and the OH radical formation with a benzoic acid to confirm the possibility for photocatalytic activities of HGORs. As a result, we confirmed that HGORs, which has an increased active site due to its three-dimensional structure formed by the reaction of graphene with hydrogen, can act as an effective catalyst. In addition, we could also realize the possibility of optical applicability by observing the 0.13 eV of band gap opening of HGORs. This is because of the unusual optical and electronic properties of graphene that can be influenced by its conformation5. This dependence on conformation can be very useful in various applications including electronic devices, catalysts, and biosensors11. Hence, research aimed at finding new electronic characteristics of graphene has attracted great attention in recent times14.Following the discovery of graphene, there has been a great effort to exploit its unique properties in various applications16. If it is possible to increase the number of active sites (broken structure of sp2 symmetry) to improve catalytic properties by making structural changes while maintaining the intrinsic properties of graphene, an efficient graphene-based catalyst can be realized.Particularly with respect to the applications of graphene-based catalysts, it is likely to be a highly efficient and a low-cost useful material. However, currently-used methods to grow graphene have fundamental limitations that are related to its two dimensional inertness. In other words, the graphene itself exhibits a low catalytic activity due to the limited number of active sites that enable the catalytic reaction17. In detail, it is expected that the inherent properties of the graphene itself can be somewhat weakened by the 3D structure of the graphene derivative. On the other hand, an enhancement in reactivity is expected near the 3D structure is expected, which could increase the reactivity of the material in different ways including enhanced catalytic activity20.Recently, it has been reported that a three-dimensional (3D) graphene derivative called as hexagonal graphene onion rings (HGORs) can be synthesized by regulating the hydrogen partial pressure and it could be a candidate graphene-based catalyst materialin situ growth of graphene. Briefly, by incorporating hydrogen (H2) into graphene grown on a SiC(0001) surface, we successfully fabricated 3D HGORs by controlling the H2 partial pressure and annealing temperature. Furthermore, using scanning electron microscopy (SEM), optical microscope, Raman spectroscopy, high-resolution photoemission spectroscopy (HRPES), and angle-resolved photoemission spectroscopy (ARPES), we have compared the morphologies and electronic properties of the fabricated HGORs with those of graphene.To investigate this possibility as a useful catalyst, we fabricated 3D hexagonal graphene structures with shapes resembling onion rings  by via HRPES, because the thiol group (-SH) in TP can be oxidized to form a disulfide group (-S-S-)22. In addition, to confirm the possibility of HGORs as a photocatalyst, we also assessed the photocatalytic activities of the three tested graphene-related materials in other reactions such as 4-chlorophenol (4-CP) degradation and OH radical formation of benzoic acid (BA) in aqueous solutions, which are widely used to compare the photocatalytic properties of catalyst materials26.Following the comparison of the morphological characteristics and electronic structures of the three distinct graphene-related materials , and HGORs), we assessed their catalytic activities for the oxidation of thiophenol (TP) under 365 nm-wavelength UV light illumination 27. Figure s) (Ts = 1650 K and Ts = 1830 K) under a hydrogen gas dose of ~760 Torr. The images in Fig. s = 1650 K  where desorption of a zipper-like material at the step edges was observed. Si–C sheets were removed in a layer-by-layer fashion, first leading to large terraces with straight rims28. And at the end of this process, an atomically smooth surface is formed. Our result is also in accordance with these observations, and in our case, we find in addition, that HGORs structures can be formed when the surface is passivated by H2 with partial pressure ~760 Torr as illustrated in Supplementary Material Fig. Recently, Glass K-edge XAS. As shown in Fig. s → π* peak at 285.5 eV (marked A) and by the typical line shape of the 1 s → σ* edge at 292.4 eV (marked D). However, a closer look reveals that the intensities of B (286.5 eV) and C (289.3 eV) peaks are higher for the HGORs structure. Hence, this difference of the electronic structure can be explained to be due to contributions from peaks induced by hydrogen30. In other words, when comparing the XAS spectra for the three tested samples, the most significant difference between graphene and HGORs is the increments of these two peaks due to hydrogen doping, while the basic electronic structure of graphene remains the same (black color).To clarify the differences in electronic structure of the three tested samples, we also performed carbon et al. first reported well-formed and regularly shaped ~100-μm-wide HGORs grown on a Cu foil17. Our HGORs structure also shows a regular shape, but with a smaller width (~4.2 μm). In view of its smaller size, we expect that the HGORs fabricated in the work could be useful in practical applications. Figure From the optical microscope image in Fig. 2 partial pressure was investigated, but no significant difference was found for partial pressures in the range 700–760 Torr. Once the optimal conditions for fabricating HGORs were determined, HRPES, Raman spectroscopy, and ARPES were performed to observe the difference in the electron structure of HGORs from that of graphene.To confirm the effect of hydrogenation on the structure, the influence of Hs core-level spectra of the graphene layer grown on the SiC(0001) surface; sp2 carbon atoms at the interface layer (marked as S2), the 6√3 × 6√3 interface layer (buffer layer marked S1), the graphene layer (marked as G), and the SiC substrate (marked as SiC)32.As shown in Supplementary Material Fig. s core-level spectra  and 1320 cm–1 (D peak) resulting from the graphene layer34. In detail, comparing the intensity ratios of ID/ IG for three samples, it can be seen that m-HGORs and HGORs have values of 0.348 and 0.642, respectively, while graphene is that of 0.218. As is well known, the increased intensity ratio is a good explanation for the 3D structure formation via hydrogen doping in HGORs, which means that a new interface structure has been formed, unlike graphene. In addition, since the peak at 1627.9 cm−1, which is indicated by D’, is the peak due to the defect induced structure of the samples and the intensity of the peak of HGORs is larger than that of graphene, it can be also explained as the difference of electronic structure between graphene and HGORs. As shown in these figures, apart from the change in intensity of the peaks due to hydrogen doping, the general characteristics of graphene are maintained for the other two samples.Figure F) and the Dirac point (ED). As shown in Fig. 36. Based on this result, changes in bandgap of modified m-HGORs and HGORs were measured from the ARPES spectrum, meaning that the band gap energy was estimated from the change in energy gap between the Fermi level and the Dirac point of the two samples. From Fig. For graphene, the absence of bandgap is a major disadvantage that limits its application as an optical material. In order to have semiconductor characteristics, it is necessary to have some degree of gap opening. Therefore, it is important to determine whether HGORs has a measurable band gap. To do this, we measured the difference in band gap between the three tested samples using ARPES. Figure To evaluate the application potential of HGORs, we tested its possibility, which can act as a catalyst. More specifically, we assessed the catalytic activation of TP oxidation see Fig. , and 4-Cp core level spectra of 360 L TP adsorbed on the three tested samples under UV illumination. All the spectra show two distinct S 2p3/2 peaks at 161.5 and 162.9 eV, corresponding, respectively, to the C-SH unbound state (denoted as S1) and the disulfide state (denoted as S2)39; note that the disulfide peak is formed from by the oxidation of the thiol group of TP. Because it has been shown that disulfide is an oxidation product of the thiol group [see Eq.  against time, a first order-reaction relationship was obtained. As shown in Fig. k) for the change in concentration (ln(C/C0)) with time (min.) for the three tested samples. In detail, the apparent rate constant for the decay of the reactant for HGORs was 1.41 × 10−2 min−1 when using HGORs, faster than the cases using other tested samples (8.89 × 10−4 min−1 for graphene and 6.68 × 10−3 min−1 for m-HGORs). As mentioned above, the improvement of the catalytic properties of HGORs has previously been shown to be closely related to the 3D structure induced by hydrogen doping in he samples. Electrons and holes so produced react with aqueous 4-CP solution to generate OH radicals that are capable of effectively degrading the 4-CP solution40.Figure p-Hydroxyl benzoic acid (p-HBA) is formed. Hence, by comparing the amount of the p-HBA formed at a given time, the influence of the OH radical on the catalytic reaction can be analysed and compared26. Results in Fig. Our results are also consistent with the assumption that active radicals are formed on the surface of HGORs, since the reaction of OH ions with holes in HGORs has been shown to usually generate more OH radicals. To confirm the generation of a large number of OH radicals on HGORs, the radical reaction of benzoic acid was performed on these substrates. When the OH radical formed on the surface of a graphene-based sample reacts with benzoic acid (BA) as shown in Eq. , p-HydroThe results are also consistent with those obtained in TP oxidation and 4-CP degradation experiments. Therefore, our results proved that the 3D structure of graphene induced by hydrogen doping can enhance the catalytic activity by forming many active sites (or defect structure).Finally, HGORs exhibiting catalytic properties must be able to maintain their properties after reuse because economic considerations must be taken into consideration in order to be applicable to various fields. Therefore, the reaction by the 4-CP degradation and the OH radical formation of BA using the HGORs, which showed good efficiency among the three tested samples. In particular, because the inertness is one of the most important features of graphene, it is very important to confirm the reuse rate for HGORs in various fields.p-HBA obtained from the OH radical formation of BA were reduced by only ~10.7 and 12.7% in HGORs after 5 re-use. This result was very meaningful that the HGORs shows some practical possibility, which can be applicable in the field of catalysis. Through the additional experiments, we can confirm that HGORs shows some practical applicability because it shows some degree of re-use as well as photocatalytic properties.To test this, re-use experiments for 4-CP degradation and OH radical formation of the HGORs were run for 5 cycles shown in Fig. via HRPES. Furthermore, we also monitored the photocatalytic activity through the rate of 4-CP degradation and the concentration of OH radical formation with BA. Results of spectroscopic analyses clearly confirmed that HGORs can be potentially used as an effective catalyst with a bandgap opening (~0.13 eV) due to the creation of active sites by hydrogen doping. We further expect that the modified graphene such as HGORs will continue to be used by controlling the active sites in manufacturing the modified graphene as an efficient catalyst in the field of nanoscience.In conclusion, we successfully fabricated HGORs with a regular size (~4.2 µm width) from graphene grown on the SiC(0001) surface by reaction with hydrogen. In order to compare the catalytic activities of graphene and HGORs in oxidation reactions, we tested the three materials in the oxidation of TP D = 9 × 1017 cm–3) Si-terminated 6H-SiC(0001) was purchased from Cree Research (USA). Thiophenol (TP: 97% purity), 4-chlorophenol (4-CP: 97% purity), and benzoic acid (BA: 98% purity) were purchased from Sigma Aldrich (Korea).Nitrogen-doped  surface. Prior to graphene formation, the temperature of the SiC(0001) surface was increased stepwise to 1120 K and left overnight for outgassing the substrate. The outgassing was completed at 1200 K and the samples were then annealed at this temperature for 5 min using a silicon dozer (1 Å/min) to obtain a Si-rich surface, the sample temperature was further increased to 1500 K and held for 2 min to form the graphene layer on the substrate. The sample was outgassed again overnight after the formation of a graphene monolayer and then placed in the preparation chamber filled with H+ ion laser  excitation source and a spectrometer (Horiba Jobin Yvon TRIAX 550). HRPES, X-ray absorption spectroscopy (XAS), and angle-resolved photoemission spectroscopy (ARPES) experiments were carried out using an electron analyser  at the 10A2 beamline (Pohang Accelerator Laboratory) to identify the electronic structure of the three tested samples. The C 1 s and S 2p core level spectra were obtained by using photon energies of 330 and 230 eV respectively to enhance the surface sensitivity. The binding energies of the core level spectra were determined with respect to the binding energies (EB = 84.0 eV) of the clean Au 4 f core level for the same photon energy. For photocatalytic activity test, we used 4-chlorophenol  and benzoic acid , as target contaminants. The required amount of the three different graphene-based samples  was prepared in a reactor containing an aqueous solution of each substrate . The suspensions were magnetically stirred in the dark for 20 minutes to establish absorption–desorption equilibrium. Then, the reactor was irradiated by a 300-W Xe arc lamp (Newport) with a cutoff filter for UV illumination (the wavelength was 320 nm). Solution samples including 4-CP (or BA) were collected using a 1-mL syringe every 15 min, filtered through 0.45 µm PTFE filter (Whatman), and then, analyzed. The concentrations of 4-chlorophenol and p-HBA were measured by high-performance liquid chromatography  equipped with a diode array detector and a Shim-pack GIS column (4.6 mm × 250 mm).The sample morphology was analysed using field-emission SEM . Raman spectra data were obtained with an ArSupplementary Information.


To develop and validate a risk assessment model for the prediction of the acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) in patients with idiopathic pulmonary fibrosis (IPF).  We enrolled a total of 110 patients with IPF, hospitalized or treated as outpatients at Xuzhou Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine from July 2012 to July 2020. Of these, 78 and 32 patients were randomly assigned to training and test groups, respectively. The risk factors for AE-IPF were analyzed using logistic regression analysis, and a nomographic model was constructed. The accuracy, degree of calibration, and clinical usefulness of the model were assessed with the consistency index (C-index), calibration diagram, and decision curve analysis (DCA). Finally, the stability of the model was tested using internal validation.  The results of logistic regression analysis showed that a history of occupational exposure, diabetes mellitus (DM), essential hypertension (EH), and diffusion capacity for carbon monoxide (DLCO)% predicted were independent risk factors for AE-IPF prediction. The nomographic model was constructed based on these independent risk factors, and the C-index was 0.80. The C-index for the internal validation was 0.75, suggesting that the model had good accuracy. The decision curve indicated that for a threshold value of 0.04–0.66, greater clinical benefit was obtained with the AE-IPF risk prediction model.  A customized AE-IPF prediction model based on a history of occupational exposure, DM, EH, and DLCO% predicted provided a reference for the clinical prediction of AE-IPF. Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease characterized by severe pulmonary tissue fibrosis without a known pathogenesis; the mean survival time after diagnosis is 2–3 years. The occAE-IPF was first reported in 1984. Yoshimura et al. briefly reported the symptoms and mortality rate of AE-IPF and identified the efficacy of a large dosage of glucocorticoids . It was Although multiple retrospective studies have reported on the incidence and mortality rate of AE-IPF, a consensus has not been reached. This can be attributed to the different study designs, inconsistent standards for disease diagnosis, few cases, and loss of patients to follow-up . In a reCurrently, there are no effective therapies for the treatment of AE-IPF. Internationally acknowledged suggestions include supportive nursing and glucocorticoid treatment which aim to alleviate symptoms and rectify hyperemia; however, the effects of these strategies are limited . TherefoPatients with IPF who were hospitalized in or visited Xuzhou Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine from July 2012 to July 2020 were enrolled in the present study. In total, 110 patients were included; 78 patients were selected for the construction of the risk analysis model, and the remaining 32 patients were used to test the model. For patients with multiple hospitalizations or visits, the data from the first hospitalization or visit were adopted. The patients provided written informed consent for inclusion in this study, and the study was approved by the Ethics Commission of Xuzhou Hospital of Traditional Chinese Medicine (ID: XZTCM2018LSY-013).Patients with IPF were confirmed based on clinical features, imaging data, and medical history according to the diagnostic criteria for IPF (2011) which included the following: (1) interstitial pneumonia with known etiology  and (2) HRCT-indicated UIP or suspected UIP symptoms . The diaN-acetylcysteine (NAC), pirfenidone (PFD), glucocorticoid (GC), and acid-inhibitory drugs.Clinical data were obtained from the medical history of the patients during hospitalization and the follow-up visits. The data included the following: (1) patient characteristics, such as age, sex, occupational history, and history of smoking; (2) complications, such as diabetes mellitus (DM), essential hypertension (EH), coronary heart disease (CHD), cerebral infarction (CI), hypothyroidism, gastroesophageal reflux disease (GERD), and obstructive sleep apnea-hypopnea syndrome (OSAHS); (3) lung function test indices, such as Forced Vital Capacity (FVC)% predicted and diffusing capacity for carbon monoxide (DLCO)% predicted; and (4) medical treatment, including pvalue as characteristics; a p value of <0.05 was considered statistically significant. Factors describing statistical significance were used to construct the nomogram using the “rms” program to create a prediction model. To test the accuracy of the prediction model, we used the “rms” program to calculate the consistency index (C-index). The degree of calibration was evaluated by constructing a calibration curve; the closer the calibration curve of the prediction model to the standard curve, the better the consistency of the prediction model. The area under curve (AUC) value of the model was calculated to predict the degree of discrimination of the prediction model, with a higher AUC value representing a higher degree of discrimination. By quantifying the net benefits of the probability of different threshold values in the array, decision curve analysis (DCA) was used to determine the clinical usefulness of the nomogram. Finally, an internal validation method was used to test the stability of the prediction model; the “rms” program package was used to calculate the C-index and AUC value for the 32 patients with IPF in the test group.All clinical data are shown as count (%) and were analyzed with R version 3.6.3 . The Caret package in R was used to randomly assign the 110 patients to the training and test groups in a ratio of 7 : 3 . Information on the training group was subjected to multivariate logistic regression with the odds ratio, 95% confidence interval, andIn total, 110 patients with IPF, treated as inpatients or outpatients between July 2012 and July 2020, were enrolled in the present study. In the training group (78 cases), there were 16 patients with AE-IPF (20.51%), while in the test group (32 cases), there were four patients with AE-IPF (12.50%). All the patients' clinical data, including demographic and clinical characteristics, complications, lung function, and medication, are presented in p < 0.01), DM (p < 0.01), EH (p < 0.01), and DLCO%-predicted lung function (p < 0.05) were identified as the risk factors for AE-IPF (The clinical data of the training group were entered into the multivariate logistic regression model to obtain the coefficients of the corresponding characteristic variables. Among the variables, a history of occupational exposure (r AE-IPF .Based on the results of the logistic regression, we determined the predictive factors for AE-IPF. To facilitate the estimation of the risk for AE-IPF, we screened the four characteristic variables and then constructed a nomographic risk prediction model. Each variable was scored on a scale, and the range of the total score was 0–220. The total score on the AE-IPF risk axis represents the probability of AE-IPF. The higher the score, the higher the risk that a patient with IPF will develop AE-IPF .x-axis represents the predicted risk for AE-IPF, and the y-axis represents the realization of AE-IPF. The bidiagonal dotted line represents the prediction of an ideal model, and the solid line represents the realized prediction capacity. The closer the dotted line is to the bidiagonal line, the greater the prediction capacity. The results of the present study showed a preferable consistency between the nomographic model and the ideal model.We constructed calibration curves to facilitate the assessment of the degree of calibration of the risk prediction model of AE-IPF. As shown in To test the accuracy of the model, we first calculated the C-index. An ROC curve indicates that the predictions are close to random guesses. Meanwhile, when an ROC curve is plotted, we can use AUC to measure the performance of the predictor. We then calculated the AUC value of the model and constructed an ROC curve . Based oTo further evaluate the stability of the AE-IPF risk prediction model, we calculated the C-index and AUC values of the 32 patients with IPF in the test group using the internal validation group. The C-index value was 0.75, and after constructing the ROC curve to calculate the AUC value for further validation, the AUC value was 0.70 . Therefoy-axis indicates the net benefit, and the x-axis represents the probability of the threshold value. The black solid line represents no intervention, at which the net benefit is zero. The black dotted line represents the intervention, and the net benefit is an oblique line with a negative slope. The blue solid line represents the realized profits of the AE-IPF risk prediction model. The decision curve showed that the greatest clinical benefit will be obtained from the AE-IPF risk prediction model at threshold values of 0.04–0.66.DCA was used to evaluate whether the prediction model could improve clinical decision-making. The decision curve of the prediction model is shown in At present, the cause of AE-IPF is unknown. Recurrent local outbreaks of alveolar damage may lead to the progression of IPF . For somBased on the prediction model, occupational exposure is a major risk factor for AE-IPF. Therefore, workers engaged in ore mining, construction work, inorganic dust production, and chemical processing and manufacturing, and those exposed to organic dust production, are at high risk. Owing to a lack of research, the association between occupational exposure and a strong predisposition toward AE-IPF is unknown. It is likely that when productive chemicals with systemic toxicity enter the respiratory tract in the form of aerosols or gas, they readily enter the blood flow through the vascular bed in the lung, leading to respiratory system damage, systemic poisoning, and AE-IPF.In addition to IPF itself, some patients with IPF also have other complications, which may also induce AE-IPF . AccordiLung function measurement is an important method for IPF diagnosis and for assessing the state of illness . AccordiTo evaluate the accuracy of the prediction model, we first calculated the C-index and AUC values. The C-index was 0.80, and the AUC value was 0.77, suggesting that the AE-IPF risk prediction model has an acceptable accuracy. To further examine the stability of the prediction model, we used an internal validation method, by evaluating the clinical data of the 32 patients with IPF in the test group, and then calculated the C-index and AUC values. The C-index and AUC values were 0.75 and 0.70, respectively. The results of the internal validation thus suggest acceptable stability of the AE-IPF risk prediction model.A history of occupational exposure, DM, EH, and DLCO% predicted were risk factors for AE-IPF as determined by the logistic regression analysis, and the AE-IPF risk prediction model was successfully constructed by creating a nomogram. The nomogram could clearly and intuitively predict the probability of IPF patients' progress to AE-IPF, thus supplying a reference for clinical progression. However, the current study was a single-center retrospective study with a limited sample size. Therefore, the results of this study should be confirmed in a prospective multicenter, large-scale study. In addition, hierarchical studies of patients of different ages or with different illness severities should also be performed.


It is demonstrated that net optical gain can be achieved via topologically protected four-wave mixing in a graphene metasurface. 13 W−1 m−1, i.e., more than 10 orders of magnitude larger than that of commonly used, highly nonlinear silicon photonic nanowires. These findings could pave a new way for developing ultralow-power-consumption, highly integrated, and robust active photonic systems at deep-subwavelength scale for applications in quantum communications and information processing.We study topologically protected four-wave mixing (FWM) interactions in a plasmonic metasurface consisting of a periodic array of nanoholes in a graphene sheet, which exhibits a wide topological bandgap at terahertz frequencies upon the breaking of time reversal symmetry by a static magnetic field. We demonstrate that due to the significant nonlinearity enhancement and large life time of graphene plasmons in specific configurations, a net gain of FWM interaction of plasmonic edge states located in the topological bandgap can be achieved with a pump power of less than 10 nW. In particular, we find that the effective nonlinear edge-waveguide coefficient is about γ ≃ 1.1 × 10 In the past decade, topological photonics has emerged as a rapidly burgeoning field of exploration of topological physics ideas framed in the context of photonics. This area of research began with the theoretical work by Haldane and Raghu  interactions in a graphene metasurface upon the breaking of time reversal symmetry by a static magnetic field. In particular, we show that due to large optical near-field enhancement and large life time of graphene plasmons in such metasurfaces, a net gain of FWM interaction of plasmonic edge states located in the topological bandgap can be achieved at an ultralow pump power of less than 10 nW. This remarkable feature is a direct consequence of the unusually large effective nonlinear edge-waveguide coefficient, γ ≃ 1.1 × 10We study a topologically protected nonlinear FWM process in a graphene plasmonic system. Graphene distinguishes itself as an ideal platform to study nonlinear topological photonics in several key aspects: First, graphene exhibits large nonlinearity over a broad spectral range, from terahertz to visible light. In particular, it has been shown  in The nonlinear system explored in this work is illustrated in a and air hole radius r) and the first Brillouin zone of the system used in our simulations are shown in a = 400 nm, r = 120 nm) at different magnetic fields B = 0,2,5,7,10 T are presented in EF = 0.2 eV, vF = 106 ms−1, and τ = 50 ps .We first study the linear optical response of our graphene plasmonic system by calculating its photonic band structure using a numerical approach based on the finite element method (FEM). The unit cell (with lattice constant B = 0), due to the hexagonal symmetry of the metasurface structure, Dirac cones that are protected by the parity (P) inversion and time reversal (T) symmetries exist at K and K′ symmetry points of the Brillouin zone (B ≠ 0), the time reversal symmetry of the system is broken and, consequently, the Dirac cones are gapped out, resulting in a topological nontrivial bandgap. Moreover, the width ∆v of this bandgap increases as the amplitude of the magnetic field increases.There are several notable features of the results presented in y axis, i.e., the number of unit cells along this direction is finite (chosen to be 20 in our FEM simulations), whereas the system is periodic along the x axis , with the gap width ∆v increasing as the amplitude of magnetic field increases. However, different from the band diagrams of an infinite graphene metasurface shown in The projected band diagrams along vg = ∂ω/∂k, within the topological bandgap is either positive (top edge) or negative (bottom edge).One can also calculate the gap Chern number, which is a topological invariant that characterizes the topological properties of the bandgap, to further confirm that the bandgap discussed above is topologically nontrivial and that these edge modes are topological modes  depicted by a red triangle in To gain deeper insights into the physical properties of plasmonic bulk and edge modes of the graphene metasurface, the near-field distribution of these modes propagating in a finite graphene plasmonic metasurface , corresponding to an FWM process in which a pump edge mode with wave vector kp gives rise to signal and idler edge modes with wave vectors ks and ki, respectively. In particular, the exchange of energy among the interacting waves is most efficient when the phase matching condition ∆κ = 0 is satisfied. Unlike the wave vector, the energy is conserved in the FWM process, meaning 2vp = vs + vi.To illustrate these ideas, in what follows, we analyze the circumstances in which the phase matching condition in a degenerate FWM of one-way edge modes can be fulfilled. To this end, we calculate the normalized wave vector mismatch, defined as Δκ ≡ αΔk = −5. More specifically, for frequencies inside the domain defined by this contour, energy is transferred from the pump to the signal and idler over a distance of about 105π lattice constants.Starting from the mode dispersion curves of the topological edge modes presented in vp = 13.17 THz, vs = 13.72 THz, and vi = 12.62 THz, and for which the FWM interaction is nearly phase matched (Δκ = 5.16 × 10−6). Moreover, we considered a seeded FWM process in which the input intensity of the signal is much smaller than that of the pump, whereas the input intensity of the idler is set to zero. Specifically, in our FEM simulations, we set the source input field amplitudes at the three frequencies ∣Ep∣ = 2 × 104 V m−1, ∣Es∣ = 4 × 102 V m−1, and ∣Ei∣ = 0. Last, the nonlinearity of graphene under the influence of a magnetic field of 10 T is described by a third-order susceptibility with value of χ(3) = 5 × 10−10 m2 V2 , which is more than 10 orders of magnitude larger than that of silicon photonic wire waveguides  and the off-diagonal elements  can be determined using Kubo’s formalism (0 = e2EFτ/(πℏ2), τ is the relaxation time (plasmon lifetime), B⊥, vF, and EF being the external static magnetic field perpendicular onto the graphene surface, the graphene Fermi velocity, and the Fermi energy, respectively.In our modeling of an infinite graphene metasurface, periodic boundary conditions are used for the four edges of the unit cell depicted in The surface conductivities in vp), one for the signal frequency (vs), and one for the idler frequency (vi). These nonlinear currents are coupled via the optical fields of the interacting modes, as described by the following equationsIn the nonlinear simulations, three nonlinear surface currents are defined in the Comsol software, namely, one for the pump frequency ((3) is the third-order bulk susceptibility and the thickness of graphene is assumed to be heff = 0.3 nm (Eα, α = p, s, i, are the amplitudes of the pump, signal, and idler, respectively. More details about the nonlinearity of magnetized graphene and the coupled-mode theory describing the FWM interaction of graphene edge modes can be found in the Supplementary Materials.Here, the third-order surface conductivity is defined as