In bulk soil samples organic carbon (Corg), exchangeable cations  and hydrolytic acidity (in CH3COONa-extracts) were determined. The obtained data can be used to understand the behavior of сhemical elements in soil profiles polluted by coal mining; the negative impact of mine wastes on soil salinity; when identifying pollution levels of potentially hazardous elements in soils affected by coal mining and for complex remediation of spoil heaps in Moscow brown coal basin.Waste rocks material and acid mine drainage (AMD) in sulfur coal mining areas of Moscow brown coal basin lead to significant transformation of landscape components . Most of the abandoned sulfide-bearing spoil heaps have not been reclaimed and toxic products of their weathering cause the risk of long-term soil contamination. In this article, we report original data on some physico-chemical properties and elemental composition of liquid and solid soil phases, waste dumps and AMD from twо abandoned spoil heaps of the Moscow basin and adjacent territories . Soil samples were collected from each genetic horizon of soil depth profile at sites affected by waste dumps and mine subsidence, as well as at natural sites. Waste material was sampled from the different parts of the spoil heaps. Sampling of AMD was performed in technogenic reservoirs near waste dumps. In displaced liquid phases (by ethanol) from soils and waste dump material, natural superficial waters and AMD pH-value, electrical conductivity (EC), the content and composition of readily soluble salts (by high-performance liquid chromatography (HPLC)), as well as titratable acidity (H Reported complex data are the key to understanding the geochemical processes occurring in soils polluted by coal mining.•The data can be used by researchers to understand migration and accumulation of сhemical elements in soil profiles affected by coal mining as well as to forecast the negative impact of mine wastes on soil salinity status.•The data obtained might be helpful in the identification of pollution levels and in the geochemical assessment of technogenic anomalies of potentially hazardous elements in the soils affected by coal mining.•The data may be useful for policy makers to develop programs for complex remediation of spoil heaps and forest-steppe landscapes in the Moscow brown coal basin.12+ and Fe3+) of natural superficial waters and AMD released from the spoil heaps. Data on chemical properties  of displaced liquid phases from soils and waste dump material are given in org), pH value of KCl-extracts, concentrations of exchangeable cations and hydrolytic acidity of natural soils, and soils with technogenic transformations are shown in 22.1The dataset area is situated in the northern part of Central Russian upland and belongs to the southern part of Moscow brown coal basin . Watersheds and gentle slopes are occupied by deciduous forests with lime, maple and oak and mixed-grass meadows.Natural soils of the dataset area are Greyzemic Phaeozems Albic 3  and FeCO3 (siderite) impurities in clays Due to the technology of underground mining, conical spoil heaps of waste rocks 40–60 m high were formed on the land surface. Spoils of the Moscow brown coal basin comprise of iron sulfide-bearing carbonaceous black greasy clays with kaolinitic clays, brown coal layers, loams, sandy loams, and quartz sands, as well as pyrite crystals with СаСОThe weathering of the spoil heaps led to the formation of deluvial-proluvial tailings of sandy-clay gangue with a high content of sulfides (mainly pyrite and marcasite), as well as organic carbon of coal origin. Technogenic deposits that overlap soils could be up to several dozen centimeters in thickness. The waste material and AMD released from the spoil heaps were strongly acidic (рН<4.5) due to continuous oxidation of sulfides and subsequent releasing of toxic sulfuric acid, as well as the formation of ferric and ferrous iron sulfates Because of dewatering of abandoned coal mines, in coal mining areas dips and subsidence up to 6 m deep were formed 4 salinization of soil profile along with the increasing of H+ and Al3+ ions content; (2) cation exchange, leading to the displacement of Cа2+ and Mg2+ by Al3+, H+ and by Fe2+ cations in soil ion-exchange complex; (3) accumulation of potentially hazardous elements. That resulted in the formation of technogenic variations of Greyzemic Phaeozems ‒ acidified, compacted, carbonized, base-unsaturated, and salinized soils.The key geochemical processes in Greyzemic Phaeozems at mine sites were as follows: (1) acidification and Fe-Al-SO2.2Sampling of soils, waste dump material, natural superficial waters, and AMD was performed at twо abandoned spoil heaps of Moscow brown coal basin: Smirnovskaya-6 (the key site «Кireevsk) and Skuratovskaya-6 (the key site «Tula») and adjacent territories.Soil samples were taken from the central part of each genetic horizon and in 8 soil pits (4 for each key site) down to 110–150 cm. For each horizon, three subsamples were collected from different walls of the pit to form a composite sample. Reference soils were sampled within 500 m from the spoil heaps. Soils with technogenic transformations were sampled in areas of the deluvial-proluvial deposits and the release of AMD. Two soil pits at the key site «Tula» were located in the mine subsidence area.Waste material was sampled by drilling down to a depth of 100 cm at the foothill of the Smirnovskaya-6 waste dump and the slope of the Skuratovskaya-6 waste dump.Natural surface waters samples were taken at 4 locations: from the ponds in karst holes (2 samples) and rivers (2 samples) at a distance of about 500 m from the spoil heaps. The sampling of AMD was performed at 3 points of its release (from waterlogged reservoirs), at the foothills of the waste dumps.2.3AMD and superficial waters were sampled into 500 mL chemically inert plastic containers. The containers were filled up to the lid to avoid the degassing of the water and were placed into a portable refrigerator at 4 °C. The samples of water and AMD were filtered through 0.45-μm PVDF filters  and analyzed in the laboratory using high-performance liquid chromatography (HPLC) within 24 h after sampling.n = 100) of waste dumps, technogenic deposits, and soils, were air-dried at temperature <40 °C and were crushed to a particle size of 1 μm for physico-chemical analyses. Prepared bulk samples were stored in special sealed plastic containers. Fresh soil and technogenic deposits samples were used for soil solutions displacement. Soil solutions were displaced by ethanol (Ischtscherikow-Komarova method) The collected samples  and cations  in soil solutions were measured by HPLC using a Styer chromatograph . The results of measurements of soil solutions are given in mmolc dm−3. The calculation of the ionic ratios, as well as their proportion in the sum of anions and cations, was performed for ion concentrations, expressed in mmolc dm−3. The content of H+ and Al3+ ions in soil solutions  was determined by titration to pH 8.2 using a 0.01 М NaOH solution. The total alkalinity of soil solutions was determined by acid-base titration using a 0.01М H2SO4 solution to pH 4.4. −1) was evaluated by summarizing concentrations of all determined elements.Electrical conductivity (EC) of soil solutions was measured using the conductometer SevenEasy S30 . Anions  were determined in bulk soil samples by extraction with 1 M KCl solution at a ratio soil:solution as 1:2.5. The content of exchangeable Ca2+ and Mg2+ in KCl-extracts was determined by titration using a 0.05М EDTA solution + and Al3+) was released upon exchange by a buffered 1 M KCl solution at the soil to solution ratio of 1:2.5.After the displacement of the soil solution, soil samples were removed from the columns. These samples were used to determine the exchange cations. Exchangeable cations . The pH-values of soil solutions and KCl-extracts were measured by the potentiometric method using Expert 001 ionometer .The content of water-soluble and exchangeable (KCl-extracts) Feorg in soil was determined by K2Cr2O7 oxidation method.The total content of C4P2O7 without H2O2 oxidation of organic matter. The particle-size classes were defined in accordance with the Russian conventional fraction groups The grain size distribution in soil and technogenic deposit samples was quantified using Analysette 22 MicroTec plus  laser particle sizer. Samples were pre-treated for analysis by dispersing with 4% NaThe total content of macroelements  and microelements  in technogenic deposits and soils was measured using WDXRF SPECTROSCAN MAKS-GV spectrometer  operating on the proprietary QAV software.The total content of sulfur in technogenic deposits and soils was measured using portative analyzer WDXRF «Olympus Innov-X Delta» . For XRF analysis, air-dried soil samples were crushed manually in an agate mortar to a particle size of ≤71 μm. The samples were pressed into cups 3 mm deep and specially made from boric acid.Calculation of the results of soil, technogenic deposits and waste material analyses was done on the basis of oven-dried (at 105 °C) soil mass.Alexander S. Kostin: Conceptualization, Investigation, Writing - Original draft preparation, Writing - Reviewing and Editing; Pavel P. Krechetov: Methodology, Data Curation, Supervision; Olga V. Chernitsova: Software, Visualization; Elena V. Terskaya: Validation, Provision of resources.The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.


Plasma tumour DNA (ptDNA) levels on treatment are associated with response in a variety of cancers. However, the role of ptDNA in prostate cancer monitoring remains largely unexplored. Here we characterised on-treatment ptDNA dynamics and evaluated its potential for early assessment of therapy efficacy for metastatic castration-resistant prostate cancer (mCRPC).Between 2011 and 2016, 114 sequential plasma samples from 43 mCRPC abiraterone-treated patients were collected. Targeted next-generation sequencing was performed to determine ptDNA fraction. ptDNA progressive disease was defined as a rise in the fraction compared to the pre-treatment.p = 0.0002 and OR = 6.0, 95% CI 1.6–20.0, p = 0.01, respectively). We also identified exemplar cases that had a rise in PSA or pseudoprogression secondary to bone flare but no rise in ptDNA. In an exploratory analysis, initial ptDNA change was found to associate with the duration of response to prior androgen deprivation therapy (p < 0.0001) but not to prior taxanes (p = 0.32).A ptDNA rise in the first on-treatment sample (interquartile range (IQR) 2.6–3.7 months) was significantly associated with increased risk of early radiographic or any prostate-specific antigen (PSA) rise (odds ratio (OR) = 15.8, 95% confidence interval (CI) 3.5–60.2, We found that ptDNA assessment for therapy monitoring in mCRPC is feasible and provides data relevant to the clinical setting. Prospective evaluation of these findings is now merited. However, early discontinuation of a drug that is providing some degree of clinical benefit relative to other available modalities can also be disadvantageous. Currently, standard disease evaluation recommended by the Prostate Cancer Clinical Trials Working Group 3 (PCWG3) guidelines3 specifies imaging tests and prostate-specific antigen (PSA) assessments: these guidelines now also highlights the need for the evaluation of progressive disease (PD) at established timepoints using liquid biopsies to better characterise disease biology and identify potential predictive molecular biomarkers.Prostate cancer is one of the most frequently diagnosed cancers in men and the second-leading cause of cancer-related death worldwide.4 Its promise as a minimally invasive biomarker is supported by the high concordance of detected genomic alterations with matched metastatic biopsies.6Circulating, cell-free DNA, often termed plasma tumour DNA (ptDNA) in the oncology setting, is rapidly becoming a quantitative and qualitative analyte to measure treatment efficacy.7 The presence of a low tumour fraction could be a limitation for plasma DNA analyses. However, the introduction of new genomic technologies with high sensitivity and specificity, including next-generation sequencing (NGS),8 has greatly contributed to the study of ptDNA.10The amount of tumour DNA in the pool of cell-free DNA is called the tumour fraction or tumour content and ranges greatly from almost undetectable up to 90%.14 A recent randomised phase 2 study (NCT02125357)15 combining whole-exome and/or deep targeted sequencing on ptDNA samples from 202 patients with first-line mCRPC treated with abiraterone or enzalutamide showed that genomic alterations in BRCA2, ATM, TP53 and AR were associated with treatment outcome and low pre-treatment ptDNA fraction was correlated with a good prognosis. However, the role of ptDNA change in response to treatment and its relationship with PSA and imaging assessment in prostate cancer monitoring is largely unexplored. Here we aimed to determine if plasma DNA dynamics could be used as an early assessment of therapy efficacy for mCRPC.In prostate cancer, we and others have shown an association between pre-treatment ptDNA fraction assessed by NGS and clinical outcome.Plasma samples were prospectively collected with the primary objective of biomarker evaluation approved by the Institutional Review Board of Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy (REC 2192/2013). Participants had histologically confirmed prostate adenocarcinoma without neuroendocrine differentiation, PD despite “castration levels” of serum testosterone (<50 ng/dL), ongoing luteinizing hormone-releasing hormone (LHRH)-analogue treatment or prior surgical castration. Patients received treatment with abiraterone 1 g once a day and prednisone 5 mg twice daily as first- or second-line therapy. Abiraterone was administered continuously until evidence of PD or unacceptable toxicity. Serum PSA was evaluated within 3 days of beginning therapy and monthly thereafter. Radiographic disease was assessed with the use of computed tomography and bone scan at the time of screening and every 12 weeks on treatment. The study was conducted in accordance with the Declaration of Helsinki and the Good Clinical Practice guidelines of the International Conference of Harmonisation. Written informed consent was obtained from all patients.12Serial blood samples were collected pre-treatment and, when possible, on treatment and at progression. Circulating DNA was extracted from 1 to 2 mL of plasma from each patient using the QIAamp Circulating Nucleic Acid Kit (Qiagen) and quantified using the high-sensitivity Quant-iT PicoGreen double-stranded DNA Assay Kit (Invitrogen) as previously described.AR, TP53, FOXA1, CYP17A1, SPOP and regions of loss in prostate cancer. The ptDNA fraction for each plasma sample was estimated using the CLONET computational tool estimating genomic deletions.7 This method to determine ptDNA fraction was recently shown to significantly correlate with an orthogonal method of ptDNA assessment involving targeted methylome NGS.11Targeted NGS was performed on the PGM Ion Torrent using a 316 or 318 Chip for a target of 1000× coverage for 3 PtDNA progression was considered as any increase of ptDNA from baseline value. Radiographic progression-free survival (PFS) was calculated from the first day of therapy to the date of PD or death, whichever occurs first, or last tumour evaluation. Waterfall plots showed the magnitude of ptDNA fraction or PSA decline in patients with different tumour responses at 3-month therapy . The odds ratios (ORs) and 95% confidence interval (CI) were calculated using logistic regression analysis. All p values were two sided and a p < 0.05 was considered as statistically significant. Statistical analyses were performed with the SAS 9.4 software .The primary objective of the study was to compare ptDNA fraction changes with radiographic/biochemical response after 3-month therapy. The secondary objective was to evaluate the role of ptDNA in tumour monitoring from starting abiraterone treatment to PD defined as biochemical and radiographic according to the PCWG3 criteria.p = 0.005) and higher levels of baseline haemoglobin (p = 0.0003) levels compared to post-docetaxel-treated group  at Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy . Interestingly, we identified examples of cases with a PSA flare or pseudoprogression on imaging secondary to a bone flare, but without a rise in ptDNA fraction  with an LHRH agonist and antiandrogens before abiraterone. We found that the ptDNA change with abiraterone was significantly associated with previous duration of ADT Fig. . This waOne key aim of treating CRPC is to prolong survival while maintaining quality of life and preventing needless toxic effects of ineffective treatments. Currently, tumour response and treatment benefit are determined using radiographic imaging and PSA assessment, but this may result in patient over-treatment and can be confounded by an apparent initial worsening that is in fact due to a flare secondary to bone remodelling at responding metastatic sites. Our study shows the potential of adding ptDNA assessment to routine monitoring of mCRPC patients.17 Future prostate cancer studies utilising more frequent plasma sampling should enable the identification of any lead time benefit of ptDNA assessment for progression, as has been observed in other cancers.19Of note, a rise in ptDNA was strongly associated with radiological progression or any PSA rise, but not with PSA or radiological flares, indicating potential added value.In our study, there was no association with ptDNA change and ≥50% PSA decline. Although an exploratory study, the association of ptDNA change on abiraterone with previous response to ADT at castration-sensitive prostate cancer (CSPC) provides an additional level of support to the relevance of early ptDNA assessment and suggests that patients with rapid progression on ADT are less likely to derive benefit from subsequent androgen signalling targeting even when separated by one line of treatment.The main limitations of the study are the small cohort of patients, including both taxane-naive and post-docetaxel patients, the retrospective nature of the analysis and the limited number of multiple sequential plasma samples. Another general limitation for ptDNA studies is how to define progression. As we had a baseline and an on-treatment sample for all patients, we took a pragmatic approach and considered any increase of ptDNA fraction as progression. For future studies, we would envision a combination of a cut-off for what is considered a ptDNA change as well as having two subsequent samples showing an increased ptDNA fraction to confirm ptDNA rise. Despite these limitations, the present study represents a first step towards achieving the introduction of longitudinal ptDNA fraction assessment with standard tests utilised in clinical practice. This work underlines the potential of minimally invasive liquid biopsies to better inform future decision making that have suggested circulating tumour cell detection. Serial ptDNA analysis of prostate cancer patients is now being incorporated into larger prospective clinical studies.In this report, we looked at the utility of ptDNA monitoring during abiraterone treatment. We found that ptDNA assessment associated with radiological progression or response and any decline in PSA, but also provided independent information. Future studies could support the integration of ptDNA into composite biomarker tests for early assessment of treatment benefit.Supplemental Material


Bioinformatic network and pathway analyses of identified genes (n = 235) were performed using ReactomeFIViz in Cytoscape based on the highly reliable Reactome functional interaction network algorithm. This approach merges interactions extracted from human curated pathways with predicted interactions from machine learning. Genome-wide pathway analysis showed total of 16 enriched hierarchical clusters. A total of 278 enriched single pathways were also identified . Gene network analyses showed multiple known and new targets in the p75NTR gene network. This study provides a comprehensive analysis and investigation into the current knowledge of p75NTR signaling networks and pathways. These results also identify several genes and their respective protein products as involved in the p75NTR network, which have not previously been clearly studied in this pathway. These results can be used to generate novel hypotheses to gain a greater understanding of p75NTR in acute brain injuries, neurodegenerative diseases and general response to cellular damage.P75 neurotrophic receptor (p75NTR) is an important receptor for the role of neurotrophins in modulating brain plasticity and apoptosis. The current understanding of the role of p75NTR in cellular adaptation following pathological insults remains blurred, which makes p75NTR’s related signaling networks an interesting and challenging initial point of investigation. We identified  These neurotrophins and their receptors function in an extensively well-regulated mechanism resulting in a delicate equilibrium that is highly responsive to nervous system injury and change3. Neurotrophins bind to two distinct types of receptors including p75 neurotrophin receptor (p75NTR) with a lower affinity, as well as to the tropomyosin receptor kinases (Trk) receptors, which function as tyrosine kinase receptors with high affinity and selectivity4. Precursor forms of neurotrophins acts as potent ligands for p75NTR and can induce apoptosis, while lacking affinity to Trk receptors5. The Trk receptors activate numerous downstream pathways including the MEK/MAPK pathway, extracellular signal-regulated kinase (Erk), the PI3K-Akt pathway, and the phospholipase C gamma pathway6. In contrast the p75NTR does not have intrinsic tyrosine kinase activity, but rather associates with other transmembrane proteins including the Trks, Nogo and myelin associated glycoprotein (MAG) for its downstream effects5.Neurotrophins are secreted dimeric proteins, including BDNF, the most well-known, as well as nerve growth factor (NGF), neurotrophin-3 (NT3), and neurotrophin-4 (NT4)7. Similarly, a down-regulation of both Nogo and p75NTR promoted improved stroke recovery in another pre-clinical model of middle cerebral artery occlusion8. Nogo associates with p75NTR by MAG, oligodendrocyte myelin glycoprotein or Nogo that activates RhoA leading to inhibition of neurite outgrowth5. In-line with downregulation exhibiting possible neuroprotective and stimulatory effects, activation of p75NTR by proneurotrophins in culture induces a pro-apoptotic effect on neurons9. This is particularly interesting since cleaved neurotrophins promoted recovery through Trks9. In another model of central nervous system (CNS) injury, a small-molecule modulator of the p75NTR receptor demonstrated that modulation of this receptor and its associated pathways results in improved outcomes following traumatic brain injuries10. All of these studies point to the strong role of neurotrophins and p75NTR in the response to CNS injury.This modulating role of p75NTR have been shown in several animal model studies. Inhibition of p75NTR and its associated Nogo co-receptor exerts a neuroprotective effect in models of middle cerebral artery occlusion11. This mechanism of p75NTR mediated apoptosis without Trk receptor activation likely plays a significant role in AD pathogenesis11. Interestingly, further findings by Yao et al. (2015) demonstrated physiological neuroprotective effects of p75NTR ectodomain against amyloid β peptide toxicity in the brain of AD patients12. In addition, AD patients have increased levels of p75NTR and proBDNF in hippocampal tissue samples and a greater proBDNF/BDNF ratio in cerebrospinal fluid that may result in imbalance of death and survival counter-regulation mechanisms13. Of note, in AD an increased level of proNGF leads to p75NTR activation and apoptosis in the absence of TrkA14. P75NTR has been shown to have similarities in the biology of Amyloid precursor protein (APP) which plays a significant role in AD patophysiology15. Also, patients with amyotrophic lateral sclerosis (ALS), another neurodegenerative disease, revealed highly increased p75NTR ectodomain concentrations in urine when compared to healthy controls. In addition, high concentrations of p75NTR ectodomain correlated to progression of ALS disease16.Viewing the role of neurotrophins in exhibiting neuroprotective effects from a slightly different viewpoint, it has previously been suggested that Alzheimer’s disease (AD) amyloid β peptide also acts as ligand to p75NTR, but not Trk receptors while inducing neuronal apoptosis18. With p75NTR being studied in various different diseases and fields suggests that it may play an important central role in cellular response to injury. The body of evidence surrounding p75NTR is diffusely spread out over numerous fields suggesting that a review of these separate bodies of work in one study may identify new connections, pathways, and mechanisms that were previously identified as isolated characteristics.Understanding the role that p75NTR and associated molecules may play in AD also supports the notion that these molecular mechanisms should not be underestimated in the pathophysiological response to acute brain injury. Especially when considering the significant role that plasticity and associated adaptive mechanisms may play in these insults, including chronic traumatic encephalopathyp75NTR interaction networks and pathways to understand how these genes form networks, connections, and signaling pathways across the broad literature that involves p75NTR. By using machine learning educated linkage gene analysis, we aimed to identify new gene and protein candidates that may be involved in a network with p75NTR, but have not been widely identified or established as possible targets in the literature regarding acute and chronic response to cellular injury.Herein, we investigate the published literature through machine-learning approaches to study the role of p75NTR and its related gene networks in an attempt to elucidate new factors that may be important to future development of therapeutic targets. We hypothesized that by using already published results of p75NTR signaling, we would be able to investigate p75NTR and its related genes, we performed an extensive literature search using PubMed on published articles from the past 20 years (1998/11/13–2018/11/13) related to p75NTR. The final query term was: (p75[All Fields] AND ("neurons"[MeSH Terms] OR "neurons"[All Fields] OR "neuron"[All Fields])) OR (p75[All Fields] AND ("brain"[MeSH Terms] OR "brain"[All Fields])) OR  AND ("1998/11/13"[PDAT] : "2018/11/13"[PDAT]). The query resulted in a total of 2041 publications. We used R statistical software with “PubMed.mineR” package to mine all gene names appearing in the 2041 publication abstracts19 (Core-team R 2015). Appropriate gene names were used in accordance with HUGO Gene Nomenclature Committee (HGNC) approved symbols (genenames.org). The results of data mining were adjudicated by the authors JK and AS.In order to identify p75NTR gene and its role in gene networks and pathways. Network analyses were performed with ReactomeFIViz in Cytoscape (https://www.cytoscape.org/) based on a highly reliable Reactome functional interaction (FI) network. The entire FI network was constructed by merging interactions extracted from human curated pathways with interactions predicted using a machine learning approach21. Network analyses were followed with Reactome pathway analyses, and false discovery rate (FDR) corrected p-value < 0.05 was considered to be significant in order to avoid false positive results22. Visualization of genome-wide pathway enrichment analysis and generation of hierarchical cluster figure was performed with Reactome analysis tool provided in reactome.org20.A flow of the study is presented in Fig. Institutional ethics board evaluation was not applicable due to the nature of this study. Good scientific practice was used throughout this study.p75NTR and its role in neuronal signaling . The five most enriched pathways  were pathways in cancer, signaling by interleukins, signaling by NGF, melanoma, and proteoglycans in cancer. A list of the top 20 pathways are presented in Table We further identified enriched pathways related to 235 p75NTR and related genes connect to each other with related functions  genes and linkage genes in relation to the p75NTR’s gene networks and pathways. We identified p75NTR related genes, gene networks, and pathways while identifying new associated p75NTR network genes. While a portion of these results validate previously published mechanistic links, the advantage of reviewing vast amounts of previously unconnected literature through this approach also identified new genes and proteins for future studies. The pathway analyses provide a general overview of the functions of p75NTR in a network with its closely related genes. The data presented here will also serve as a useful resource to the research community in querying potential biomarkers, therapeutic targets, and potential areas of future studies.The complex role of p75NTR in brain plasticity and apoptosis has proven a challenging subject of investigation leaving researchers without a full understanding of the role it plays in various pathophysiological aspects. Herein we investigated previously published literature using data mining and machine learning approaches to gain a better understanding of 27. The most enriched pathways identified validated the roles of previous studies, such as the finding that p75NTR has been shown to be highly expressed in human cancers including melanoma29. The link of p75NTR to cancer pathology is particularly interesting, given its role in immune modulation, matrix remodeling, and cellular adaptation. However, there are currently no studies demonstrating p75NTR’s effects on modulating the immune response in cancer, nor in acute or chronic brain disease suggesting this area may warrant further investigation, noting also the fact that p75NTR belongs to tumor necrosis factor receptor superfamily. The pathway analyses that resulted provide a general overview of the functions of p75NTR in a network with its closely related genes. Overall, 278 enriched pathways and 16 hierarchical pathway clusters were identified generating vast amounts of data that may be useful for validation and future research by other investigators.Our results obtained from genome-wide cluster and pathway analyses validated the current understanding of p75NTR in that it plays a role in various pathways including programmed cell death, immune system modulation, signal transduction, developmental biology, gene expression regulation, and extracellular matrix organizationp75NTR identified genes. GRB2 has been shown to be directly downstream of TrkA acting as a signaling adaptor protein30. Associations or links of p75NTR and GRB2 are not well established. Interestingly, in the brain tissue of scrapie infected rodents, BDNF, TrkB, phospho-TrkB, GRB2 and p75NTR were all significantly down-regulated supporting the direct association identified in our results between GRB2 and p75NTR32. Viral infection etiology of AD has been a widely known controversial topic. This makes the finding about scrapie viral infection particularly interesting. Results of three independent AD cohorts showed disruption of molecular, genetic, and clinical networks by human herpesvirus33. However, the role of p75NTR in possible viral pathophysiology of AD remain unknown, but these results in conjunction with previous works may shed some light generating new hypotheses and studies.The gene network analyses in combination with the focused subnetwork analysis using linkage genes identified functionally related genes that were not part of our original datamined UBC, the gene for polyubiquitin precursor protein, which is known to have a role in proteasome degradation34. Conjugation of ubiquitins has been well established as highly important for protein degradation and the associated role that plays in larger cellular process such as DNA repair, cell cycle regulation, kinase modification and endocytosis system35. Loss of UBC is associated with the pathophysiological molecular factors of AD via decreased proteasome degradation system, which may be thought of as cells inefficiently removing malfunctioning, damaged, or old proteins36. These connections with p75NTR underline the important role in AD’s pathophysiology17. However, it is important to understand that the role of p75NTR in AD pathophysiology is only a model for which p75NTR likely acts as a central protein signaler in response to various elements of cellular damage.Another linkage gene identified was 38. As shown in several studies, MAPKs are downstream targets of Trks and p75NTR39. Our results suggested that NGF and p75NTR link with MAPK1, and NGF activates MAPK3, which are in line with previously published studies40. An interpretation for these interactions in the broader context of a disease can be seen with inhibition of ERK1/2 following the acute phase of stroke promoting long-term functional outcome and enhanced later-stage recovery processes in rats41. Other experiments using preclinical rat models have shown how MAPK activity also plays a fundamental role during postnatal neurogenesis when hippocampal apoptosis and synaptogenesis are occurring42. The significant connections between MAPK and p75NTR highlight that p75NTR should also be further investigated in relation to these disease processes, as well as finding possible targets for future medical therapy.MAPK1/ERK2 and MAPK3/ERK1 pathways were also identified and their roles have been reported in apoptosis, neuronal repair, and axonal growthSRC, CTNNB1, and EP300 were identified in the same linkage gene network with p75NTR. Interestingly, inhibition of SRC family kinases improved cognitive function in rats after intraventricular hemorrhage43. Indeed, these observations further increase the interest of p75NTR and its role following hemorrhagic insult as another form of pathological damage cells suffer acutely. Similarly, CTNNB1 encodes a β-catenin protein that increases proNGF leading to p75NTR activation ultimately promoting neuronal growth44. Further investigation showed that modulation of β-catenin pathway was neuroprotective after intracerebral hemorrhage in rats45. The role of p75NTR, however, was not previously studied in these aspects. Our results suggest an association between CTNNB1 and p75NTR possibly demonstrating a substantial role for p75NTR in the stroke response.In addition, SORT1), lingo (LINGO1) and NRAGE (MAGED1) have been linked to p75NTR functions regulating apoptosis, axonal outgrowth and transporting pro-neurotrophins39. The genes did not result in the analyses directly. Interestingly however, MAGEL2 was identified in our analysis that belong to the same melanoma-associated antigen (MAGE) family than NRAGE, and shares strong homology to NRAGE and other proteins in MAGE-family. As NRAGE is involved in the p75NTR mediated programmed cell death, MAGEL2 is linked to neurodevelopmental disorders such as Prader-Willi syndrome and Schaaf-Yang syndrome. This suggest significant importance of MAGEL2 in neuronal development in humans46. Previous animal studies have shown that mTOR and autophagy pathways are dysregulated in Magel2 null mice models47. In our analysis, MAGEL2 was directly linked to transcription factor E2F1 that was directly linked to p75NTR. This suggests a role of necdin-related MAGE proteins in p75NTR functions, supported by preclinical observations48.Previously, sortilin (UBC, and EP300 have not been extensively studied in the context of brain plasticity. These two linkage genes and their encoded proteins could be interesting targets for future studies to explore in patients with acute brain injury or neurodegenerative diseases. This also highlights the benefit of taking large scale approaches through data mining, which allows for an overview of the interactions that may exist between different studies, but have not previously been discovered to exist.Two of seven subnetwork linkage genes identified here, namely Our study was limited to in silico analyses, and confirmatory in vitro or in vivo analyses were not performed. However, the research approach used herein has the advantage of interrogating a large dataset through a systematical approach which incorporates all currently available knowledge. Analyzing such a search with powerful bioinformatic tools educated with machine learning algorithm from human curated pathways allows for broad investigation of connections that may have previously been missed, or not looked for in the first place. Confirmation of discussed associations are warranted.p75NTR. This study incorporates current knowledge using a large dataset approach that increases the overall understanding of complex p75NTR networks. These results suggest both new possible target genes for further investigation in p75NTR research, while also validating previously conducted research in identifying pathways, genes, and clusters that highlight p75NTR’s biological function. These results can be used to generate novel hypotheses to gain a greater understanding of p75NTR in acute or chronic brain injuries, other neurodegenerative diseases, and the general response to cellular injury.We provide the single largest comprehensive gene and functional network library of Supplementary Information 1.Supplementary Information 2.


Catalases (CAT) and superoxide dismutases (SOD) represent two main groups of enzymatic antioxidants that are present in almost all aerobic organisms and even in certain anaerobes. They are closely interconnected in the catabolism of reactive oxygen species because one product of SOD reaction (hydrogen peroxide) is the main substrate of CAT reaction finally leading to harmless products . It is therefore interesting to compare the molecular evolution of corresponding gene families. We have used a phylogenomic approach to elucidate the evolutionary relationships among these two main enzymatic antioxidants with a focus on the genomes of thermophilic fungi. Distinct gene families coding for CuZnSODs, FeMnSODs, and heme catalases are very abundant in thermophilic Ascomycota. Here, the presented results demonstrate that whereas superoxide dismutase genes remained rather constant during long-term evolution, the total count of heme catalase genes was reduced in thermophilic fungi in comparison with their mesophilic counterparts. We demonstrate here, for the newly discovered ascomycetous genes coding for thermophilic superoxide dismutases and catalases (originating from our sequencing project), the expression patterns of corresponding mRNA transcripts and further analyze translated protein sequences. Our results provide important implications for the physiology of reactive oxygen species metabolism in eukaryotic cells at elevated temperatures.  All these substances exhibit a rather high (and diverse) reactivity towards numerous cellular macromolecules, mainly including nucleic acids and proteins. At their elevated concentrations they can lead to irreversible damages and even cell death. It is therefore logical that in all aerobically metabolizing organisms (both prokaryotic and eukaryotic), a battery of antioxidants occurred to cope with such toxic and deleterious substances. Three major groups of antioxidant enzymes are represented by superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx). They evolved over long time periods in living systems to play a fundamental and indispensable role in the antioxidant protective capacity of most known biological systems mainly against free radical attacks [2O2) and molecular oxygen (O2) by superoxide dismutase (Equation (1)). When uncontrollably accumulated, H2O2 can be potentially harmful to body tissues or cells. In contrast, at very low concentrations it has apparently a signaling effect—as reported mainly for its production by NADPH oxidases (NOX) [2+ that can frequently occur in various cells, it is converted to deleterious hydroxyl radical (*OH) and hydroperoxyl radical (*OOH) through Fenton reactions ). In order to prevent this phenomenon, catalase, which is in eukaryotes abundant mainly in specific organelles, peroxisomes, rapidly breaks down H2O2 into water and molecular oxygen (Equation (3)), consequently curtailing free radical-induced damage. In rare cases, when the catalase is absent, like in most natural mitochondria, the majority of the health-span functions of catalase [Molecular oxygen , only about 50 species have been found to be able to grow above 50 °C [It shall be noted that a real heat tolerance or even a thermophilic character is not very common among all phyla of eukaryotes. From the scope of an estimated 3.0 million fungal species existing in the nature, and around 500,000 of them already described  CBS 180.67 was obtained from CBS-KNAW Collection, Westerdijk Fungal Biodiversity Institute, Utrecht, The Netherlands. During all experiments, the fungus was cultivated on Malt Extract Peptone Glucose (MPG) medium  or stored on MPG agar plates  at 45 °C. The strain of 260nm/280nm = 1.84, A260nm/230nm = 1.90 was used for genome sequencing. The concentration of this sample was measured with Nanodrop 2000 .Genomic DNA from 100 mg of frozen mycelium of this fungus was isolated with GeneJET Plant Genomic DNA Purification Kit  according to manufacturer’s instructions. Control agarose gel electrophoresis of isolated gDNA in 0.7% TAE buffer showed acceptable high molecular weight profile (not shown). Sample with a concentration of 58.4 ng/µL and absorbance ratios AChaetomium thermophilum var. dissitum was performed with Illumina MiSeq technology using the SPAdes 3.10.1 approach. Genome coverage of this sequencing was 16-fold. This entire genome was deposited in GenBank under accession number JAAFKN000000000 for the Bioproject PRJNA595853. For prediction of functional genes coding for antioxidant enzymes in this genome, Hidden Markov Model (HMM)-based methods, FGENESH and FGENESH+, located at www.softberry.com [Chaetomia genomic sequences were used.Genome sequencing of Chaetomium thermophilum var. dissitum was grown in MPG medium with or without the addition of 2 mM H2O2 to cells in the exponential phase of growth. Total RNA was isolated from 30 mg of fresh or frozen mycelia obtained from such MPG medium by using RNeasy Plus Mini Kit  according to the manufacturer’s instructions with a modification in homogenization step [The fungus ion step . Isolatehttp://bioinfo.ut.ee/primer3/). Reactions were amplified in a thermocycler  with the following program: initial denaturation 2 min at 95 °C; 35 cycles 15 s at 95 °C, 15 s at annealing temperature  according to the manufacturer’s instructions. Clones were selected on LB plates with kanamycin (50 μg/mL) or ampicillin (100 μg/mL), and selected clones were verified by barcode sequencing with M13 or specific primers. Sequencing was carried out by GATC Biotech, Constance, Germany.Obtained DNA samples were used as templates in PCR for a detection and amplification of superoxide dismutase and catalase genes and for confirmation of transcription of these genes into mRNA (at the level of cDNA). PCR and RT-PCR amplifications were performed using Elizyme HS Robust DNA Polymerase . Each PCR reaction contained DNA template of appropriate concentration, 1x HS Robust MIX, and 0.4 μM of a specific DNA primer pair. Primers for detection of superoxide dismutase and catalase genes were designed by us  [Three-dimensional homology models for yet unknown structures of thermostable superoxide dismutases and heme catalases were obtained from Phyre-2 server  by emplotiProt/)  and dispMyceliophthora thermophila, Thielavia terrestris, Thielavia heterothallica, Thermomyces lanuginosus, Thermomyces thermophilus, Rhizomucor miehei, Talaromyces cellulolyticus, and Malbranchea cinnamomea. In general, they reveal a surprisingly high level of genetic diversity [Chateomium thermophilum var. dissitum. This is a thermophilic filamentous ascomycete in the order Sordariales, living in soil, dung, and compost heaps at temperatures up to 60 °C [C. thermophilum var. thermophilum, which was reported previously by Amlacher, mainly focused on the presence of genes coding for nucleoporins of high thermal stability [Chaetomium cochliodes (34.75 Mb) [C. thermophilum var. dissitum are listed in We have reconstructed the detailed molecular evolution of two most important enzymatic antioxidants: superoxide dismutases and heme catalases within the fungal kingdom. These antioxidants are apparently interconnected in the catabolism of reactive oxygen species (cf. Equations (1) and (3) in iversity . We now to 60 °C . This netability  and aftetability . It is i4.75 Mb) . None ofC. themophilum var. dissitum (details in 2O2 was added).From this output we can observe that the size of spliced mRNA transcripts in the form of produced cDNA corresponds with the size of predicted exons in both SOD and CAT genes of 2−) into molecular oxygen and hydrogen peroxide ) and thus rapidly decrease the O2− level, which damages aerobically metabolizing cells at excessive concentrations [3, and H2O2. There are also some novel SODs discovered in the past decades, such as cambialistic SOD and nickel SOD (NiSOD), the former could function well either with iron or manganese at its active site [Superoxide dismutases are rather small but very compact metalloenzymes that are found in all kingdoms of life in a variety of forms with ancient origins. These proteins catalyze the dismutation of superoxide anion free radical . All representatives of Clade 1 have shorter sequences (in average only around 155 amino acids). The second clade .The evolutionary relationships for CuZnSOD family with main focus on fungal genes are presented in Candida albicans) exclusively shows the presence of Cu ions in the active center . Thermophilic manganese superoxide dismutases are likewise closely related with mesophilic soil counterparts from the class Sordariomycetes represented by Chaetomium cochliodes or by another interesting extremophile Sodiomyces alkalinus [Gauemannomyces tritici are more distantly related to them. The differences in the primary structure of FeSOD vs. MnSOD can be clearly seen from the multiple sequence alignment presented in The evolutionary relationships for FeMnSOD family with main focus on fungal genes are presented in lkalinus . Phytopa2O2. They can be divided into heme and nonheme catalases. Nonheme (manganese) catalases were not detected among eukaryotes yet and are not the focus of this research. All heme catalase superfamily members function in the rapid decomposition of H2O2 to harmless products (cf. Equation (3)). Hydrogen peroxide is a frequent by-product either from the SOD-catalyzed reaction or also a by-product of various oxidases or it can occur in xenobiotic mixtures from extracellular environmental stress. The enzyme uses heme  as a cofactor and catalyzes the heterolytic cleavage (comprising a reduction and concomitant oxidation cycle) of hydrogen peroxide to water and molecular oxygen, thus completing the cellular detoxification process initiated by various SODs. Catalases react in this way by finally limiting the effective peroxide concentration to physiologically acceptable levels [Catalases are registered in enzyme databases as hydrogen-peroxide:hydrogen peroxide oxidoreductases. They are frequently occurring antioxidant enzymes present in both prokaryotic and eukaryotic cells that can both reduce and oxidize He levels . Most ofe levels . AlthougIt shall be noted that this reaction scheme is typical for all kinds of peroxidases and “A” in this equation can be numerous 1- or 2-electron donors of various types e.g., methanol, ethanol, formic acid, or phenols and their substituted derivatives as well as aromatic amines . BifunctAlthough there are several differences in the primary structure among numerous typical heme catalases, the unique three-dimensional structural fold appears to remain well conserved . CatalasThermus thermophilus HB8 [Geobacillus sp. CHB1 [Geobacillus thermopakistaniensis [Deinococcus radiodurans [Chaetomium thermophilum var. dissitum [d besides (much more frequently occurring) heme b as the prosthetic group in their active centers [Microbial catalases are still preferred in most biotechnologies due to their economic feasibility, high production yield, ease of product modification and optimization, regular supply due to absence of seasonal fluctuations, and rapid growth of microbes on an inexpensive media. Most preferred are catalases produced from alcali-thermophilic microorganisms because of their ability to withstand high temperature and pH conditions. Numerous thermostable catalases have already been found in extremely thermophilic bacteria e.g., ilus HB8 , Geobacisp. CHB1 , Geobacianiensis , and Deiiodurans  but in fdissitum . We havedissitum  can be c centers . As obviMultiple sequence alignment of selected heme catalase sequences is presented in Homology model of CthedisKat2—representing the extracellular large-subunit catalase—is presented in Chaetomiaceae. Whereas the mesophilic Chaetomium cochliodes contains up to four genes for typical catalases , the count of these genes is reduced to three in Chaetomium thermophilum var. thermophilum. But in Chaetomium thermophilum var. dissitum , one gene for a large subunit catalase revealing some deletions (Superoxide dismutases and catalases constitute a combination of physiologically complementary enzymatic antioxidants appropriately called “first line defense antioxidants” . They boeletions  was probThe potential application of both these physiologically closely connected enzymatic antioxidants is promising in both medicine and biotechnology. As already well documented for both recombinant yeasts ,34 and mWe have reconstructed detailed molecular phylogeny of three important gene families coding for main antioxidant enzymes with focus on thermophilic eukaryotes. We can conclude that there are similar and conserved evolutionary patterns mainly between the phylogeny of fungal CuZnSOD family and fungal heme catalase superfamily. A designer protein fusion between large subunit heme catalase and a superoxide dismutase based on analyzed sequences here is suggested. It is based on replacement of a noncatalytic domain in large thermostable catalase with an appropriate thermostable CuZnSOD1 or MnSOD domain of comparable length. A close interplay of thermostable SOD and CAT is attractive for consideration in future medical and biotech applications.


Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype. Recent studies have shown that MHC class II (MHCII) expression and tumor infiltrating lymphocytes are important prognostic factors in patients with TNBC, although the relative importance of lymphocyte subsets and associated protein expression is incompletely understood. NanoString Digital Spatial Profiling (DSP) allows for spatially resolved, highly multiplexed quantification of proteins in clinical samples. In this study, we sought to determine if DSP could be used to characterize expression of MHCII and other immune related proteins in tumor epithelial versus stromal compartments of patient-derived TNBCs (N = 10) using a panel of 39 markers. We confirmed that a subset of TNBCs have elevated expression of HLA-DR in tumor epithelial cells; HLA-DR expression was also significantly higher in the tumors of patients with long-term disease-free survival when compared to patients that relapsed. HLA-DR expression in the epithelial compartment was correlated with high expression of CD4 and ICOS in the stromal compartment of the same tumors. We also identified candidate protein biomarkers with significant differential expression between patients that relapsed versus those that did not. In conclusion, DSP is a powerful method that allows for quantification of proteins in the immune microenvironment of TNBCs. Clinically, TNBC is defined by the absence of three biomarkers that are used to inform treatment decisions: ER (estrogen receptor), PR (progesterone receptor), and HER2. While traditional predictive biomarkers are not expressed in TNBCs, there has been considerable interest in quantifying the host anti-tumor immune response in patients with TNBC to determine risk of recurrence and predict response to immunotherapy. Using RNA-seq of patient-derived TNBCs, we have shown that elevated expression of genes in the major histocompatibility complex Class II antigen presentation pathway (MHCII) is a favorable prognostic factor that is associated with long-term DFS2. This genomics discovery led us to develop and validate a clinical prognostic assay that measures expression of MHCII and tumor-infiltrating lymphocyte (TIL) genes in formalin-fixed, paraffin embedded (FFPE) clinical samples using NanoString technology3. We recently confirmed the prognostic significance of this MHCII Immune Activation assay in two independent cohorts of patients with TNBC3. Mechanistic studies support our clinical findings, as ectopic expression of MHCII has been shown to induce Th1-mediated anti-tumor immunity in mouse models of human cancer6.Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer that is associated with high rates of local recurrence and a propensity for distant metastasesCD3D, CD4, and CD8A3. Our RNA-seq and NanoString gene expression studies were both performed using macrodissection of clinical TNBC specimens. While macrodissection enriches for carcinoma cells, both tumor and stromal infiltrating immune cells may be present in the macrodissected sample. Therefore, gene expression measured in clinical specimens is often reflective of both carcinoma cells as well as immune and stromal cells within the tumor microenvironment (TME)8. A logical question that arises is whether MHCII gene expression in patient tumors is simply a reflection of immune cells within the TME, or whether MHCII is expressed within tumor epithelial cells.We have shown that expression of MHCII mRNA in clinical samples is correlated with the presence of tumor-infiltrating lymphocytes (TILs) and with elevated expression of TIL genes including 9. Furthermore, quantification of IHC is often performed using a semi-quantitative scale that can be subject to interobserver variability, particularly in the research setting10. Laser capture microdissection (LCM) is one method that can be used to isolate cell types and quantify protein and gene expression in distinct populations of cells, though it is technically challenging and difficult to scale8. NanoString Digital Spatial Profiling (DSP) is a highly multiplexed method that allows for protein quantification in spatially resolved, morphologically distinct areas within a single FFPE tissue section15. This method utilizes UV-photocleavable oligonucleotides that are conjugated to primary antibodies15. After incubation with a multiplexed cocktail of primary antibodies, oligonucleotides are cleaved in regions of interest (ROIs) using UV LED light and are then hybridized with NanoString optical barcodes for ex situ counting on an nCounter Analysis system15. In this study, we sought to determine if DSP can be used to characterize expression of MHCII and other immune related proteins in tumor epithelial versus stromal compartments of patient-derived TNBC samples.Protein expression and localization in clinical FFPE samples can be assessed using immunohistochemistry (IHC) or immunofluorescence (IF); however, the capacity for multiplexing is generally limited due to spectral overlap16. The first main goal of this study was to determine whether DSP could be used to quantify proteins in morphologically distinct compartments within patient-derived TNBCs (N = 10). We selected distinct tumor cell epithelial and stromal ROIs and performed protein expression profiling using DSP; three epithelial and three stromal ROIs were selected from each tumor  such as dendritic cells, B cells, and macrophages, although previous studies demonstrate that a subset of patient-derived TNBCs and TNBC cell lines can also express genes in the MHCII pathwayors Fig. . Pancytoors Fig. . Importaors Fig. . We thenHLA-DRB1 mRNA in patient-derived TNBC tumors is significantly associated with long-term disease-free survival (DFS)3, though it has been unclear whether stromal or epithelial expression is more predictive of patient outcomes. Using DSP, we found that HLA-DR protein expression in both epithelial and stromal ROIs was significantly higher in patients with long-term DFS when compared to patients that relapsed , dendritic cells (CD11c), cytotoxic T-cells (CD8A), and B cells (MSA4A1/CD20). In summary, DSP provided a novel and powerful method examining the immune microenvironment of TNBCs and identifying proteins associated with prognosis.3. All specimens were de-identified by the Huntsman Cancer Institute Biorepository and Molecular Pathology Shared Resource facility. For this study, a subset of tumors (N = 10) with adequate pathologic material in the archives were selected for protein expression profiling using NanoString DSP. In order to interrogate the immune microenvironment of TNBCs with disparate clinical behavior, we selected samples from patients that experienced disease recurrence (N = 5) and samples from patients that remained disease-free (N = 5) after a median follow-up time of 7.4 years. Clinical characteristics of these patients are provided in Supplementary Table This project was performed under an approved University of Utah IRB protocol #24487). Since the tissue specimens were leftover, de-identified archival FFPE blocks, the IRB waived the requirement for informed consent. Patient-derived TNBC specimens were collected as previously described4487. Sin26. Using the nCounter Prep Station, hybridized probes were immobilized in a cartridge, excess probe was washed, and the cartridge was transferred to the nCounter Digital Analyzer for analysis. All DSP values for the 39 markers are included in Supplementary Table The DSP assay was performed on FFPE TNBC tumor sections through the NanoString Technology Access Program service. The specimens were sent to NanoString and ROI selection was performed through a web-based interface by a study team pathologist with the assistance of NanoString scientists. The DSP workflow can be divided into four main steps: (1) Tissue staining and imaging (2) Region of interest selection; 3) Cleavage and aspiration of oligonucleotide tags; 4) Hybridization and counting using the NanoString nCounter system. For this study, tissue sections (4 µm) were stained using a multiplexed cocktail of primary antibodies; each antibody was conjugated to a unique, UV-photocleavable indexing oligonucleotide tag. For this experiment we included all DSP validated antibodies-oligo conjugates available at the time of the study. A complete list of protein targets is provided in Supplementary Table  Cleavage Hybridiz3. Briefly, 4 µm FFPE sections were stained using an HLA-DR antibody (Santa Cruz Biotechnology (sc-53319) in a Ventana BenchMark™ ULTRA autostainer. Sections were de-paraffinized and subject to antigen retrieval using Cell Conditioning 1  for 64 minutes at 95 °C. The primary antibody was applied at a concentration of 1:1000. Interpretation was performed as previously described3.IHC staining for HLA-DR was performed as previously described27 was used to identify proteins with significantly different normalized counts  between stromal and epithelial ROIs as well as tumor from patients who recurred and those who did not. Heatmaps of log normalized protein counts were created using the R package ‘pheatmap’ version 1.0.10. Differences between groups were analyzed using Fisher exact test, 2-tailed t test with Welch correction, or 1-way analysis of variance with post hoc Tukey test.Statistical analyses were performed using GraphPad version 7.0 C and R version 3.5.0. Raw counts for each of the photo-cleavage fluorescent probes corresponding to each of the proteins measured in the DSP assay were normalized to engineered RNA sequences (ERCC RNA controls) to correct for background signal in the assay. DESEQ2 version 1.20.0Dataset 1.Dataset 2.Dataset 3.Dataset 4.


The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.Gene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment,  The concentration of cfDNA is relatively high and stable in blood, and cfDNA has therefore become a widely used analyte in liquid biopsy. cfDNA is derived mainly from apoptotic and necrotic cells of primary tumors, circulating tumor cells, and normal cells4 and is usually bound to mononucleosomes rather than present as free DNA5. ctDNA makes up only a small proportion of the total plasma cfDNA6, requiring a large volume of plasma and sensitive detection methods, and cannot be used to detect cancer when there is a low ctDNA:cfDNA concentration ratio or no mutation7.Noninvasive tests offer a number of compelling advantages, and liquid biopsies have been developed as a valuable tool over the past decade, in particular for chromosomal aneuploidy screening and companion diagnostic testing. Blood is generally the easiest specimen type to work with. In peripheral blood, testing may target circulating tumor cells; circulating cell-free DNA (cfDNA), which in cancer patients contains circulating tumor DNA (ctDNA); circulating cell-free RNA (cfRNA); or circulating extracellular vesicles (EVs), such as exosomes, tumor-educated platelets, proteins, and metabolites9. A nucleosome-free region (NFR) or a nucleosome-depleted region (NDR) is usually present in the transcriptionally active core region of the gene promoter9. Nucleosome positioning relative to transcription start sites (TSSs) is directly correlated with RNA polymerase II (Pol II) binding, and genome-wide maps exhibit differential nucleosome positioning in active and silent genes10. Nucleosomes consist of 145–147 bp DNA segments wrapped around a histone octamer composed of two molecules each of the four core histone proteins , cemented to the nucleosome surface by an additional ~20 bp DNA (linker DNA)11. cfDNA fragments are around 166 bp in length12, which corresponds to the nucleosome DNA plus linker DNA.In eukaryotes, nucleosomes are repeating units of chromatin that are thought to strongly affect gene expression14, deep genome-wide sequencing of circulating cell-free DNA enabled identification of maps of nucleosome occupancy that provide a direct footprint of transcription factor occupancy. In addition, nucleosome footprint patterns in cell-free DNA are often specific to a type of cancer13. The presence or absence of nucleosomes in the TSS region of cfDNA results from expressed or silent genes in origin tissue and thus can be used to predict gene expression. Peter et al. determined that several TSSs matched with the expressed isoforms of genes from metastatic primary tumors14, and this result has been confirmed in individuals of different ages15.In previous studies16 and the American Joint Committee on Cancer17 in 2018: the Oncotype DX 21-gene assay, the Mamma Print 70-gene assay, the Endo-Predict 12-gene assay, and the PAM 50 (Prosigna) and Breast Cancer Index tests. However, these tests are usually performed using tissue biopsies, which require invasive surgery and cannot capture the entire genomic landscape of breast tumors.Conventional gene expression profiling may be used to predict prognosis and guide treatment in the early stages of breast cancer. Five multigene expression testing techniques were included in the guidelines for breast cancer published by the National Comprehensive Cancer NetworkIn the present study, to explore relationships among the cfDNA nucleosome profile, intracellular nucleosome positioning, and gene expression, we used breast cancer cell line supernatant to mimic plasma cfDNA and sequenced it using next-generation sequencing technology. Simultaneously the cell particles were subjected to MNase sequencing and mRNA sequencing. We analyzed correlations by comparing the nucleosome footprint profiles 1 kb upstream and 1 kb downstream of TSSs, in particular at the exact positions of TSSs. Furthermore, plasma cfDNA footprint profiles were characterized by low-coverage sequencing, and differences in profiles in TSS-adjacent regions were analyzed between healthy individuals and patients and between responders and nonresponders to neoadjuvant epirubicin-cyclophosphamide-docetaxel chemotherapy. Finally, we used plasma collected before and after treatment to assess correlations between cfDNA footprint profiles and response to breast cancer treatment and to examine changes in the footprint associated with treatment.A study flowchart that includes analytical methods is shown in Fig. 14. Next we analyzed chromosome 12p11.1, a 76 kb region containing more than 400 nucleosomes with strong positioning properties by cfDNA-seq and MNase-seq for cell line supernatant and plasma cfDNA from 50 breast cancer patients. The cfDNA read depth map showed a crest pattern whose position was highly correlated with that found in the MNase map, in particular in plasma DNA , which is consistent with previous reportsWe also screened highly expressed genes (TPM > 10) and unexpressed genes (TPM = 0) in these two cell lines using mRNA-seq, then analyzed the genes’ sequence coverage depth around TSSs using MNase-seq and cfDNA-seq. The results of MNase-seq showed that the sequence coverage depth around TSSs was significantly lower for highly expressed genes than for unexpressed genes Fig. . Analysep < 10−22) and LTSSs pairs (p < 10−22). This significant enrichment was not observed in overlaps between cell-free HTSSs and intracellular LTSSs or cell-free LTSSs and intracellular HTSSs , and the HTSSs of cfDNA were highly consistent with the LEGs (p = 3.156 × 10−10). However, this phenomenon was not observed in the HTSSs of cfDNA and the corresponding HEGs or in the LTSSs of cfDNA and the corresponding LEGs  or LTSSs (low coverage depth around TSSs) and intracellular HTSSs or LTSSs revealed significant enrichment for HTSSs pairs  but not for HTSSs of highly expressed genes : 244 TSSs with relatively high coverage and 170 TSSs with relatively low coverage in patients with breast cancer (n = 85) compared with healthy donors (n = 85)  were observed using receiver operating characteristic  in late-stage cancer group  related to estrogen signaling pathway in luminal A group  differed significantly in the 28 responders compared to the 10 nonresponders: 100 TSSs had high coverage and 132 TSSs had relatively low coverage in responders ; necrotic cell death ; the intrinsic apoptotic signaling pathway in response to DNA damage ; positive regulation of reactive oxygen species biosynthesis ; and the PID angiopoietin receptor pathway . It is interesting that these genes had high TSS coverage in responders. However, several pathways were found in nonresponders, including pathways involved in pyruvate metabolism and the citric acid (TCA) cycle (R-HSA-71406) and protein O-linked glycosylation (GO:0006493). Significantly different pathways are listed in . Gene functional annotation analyses showed that these genes was related to reveal the top 15 pathways, including regulation of PTEN gene transcription (R-HSA-8943724), PID INTEGRIN A481 PATHWAY (M277), positive regulation of cell cycle (GO:0045787)  and high RCB (RCB II and III) at pretreatment, and hierarchical clustering analyses also showed an obvious separation between them Fig. . There wp < 0.01 and |log[fold change]| ≥ log1.5) were significantly differentially covered in responders: 93 of these genes’ TSS regions were downregulated and 112 were upregulated during early treatment (post-1 cycle), and 66 of these genes’ TSS regions were downregulated and 50 were upregulated during mid-treatment (post-3/4 cycles), with stable coverage after treatment (post-8 cycles). Conversely, these genes were not altered throughout the treatment period in nonresponders. Functional enrichment analyses revealed the top 20 pathways , HIC1 , and HMGB1 (high mobility group box 1) were involved in several of these pathways. Three pathways were related to cellular response to organonitrogen localization, response to estrogen, and lactation. Individual genes with significantly different coverage from these pathways are listed in Supplementary Table BRCA1], erb-b2 receptor tyrosine kinase 4 [ERBB4], GRB2-associated binding protein 1 , mediator complex subunit 1 , pro-apoptotic WT1 regulator (positive regulation of hydrogen peroxide-mediated programmed cell death [PAWR]), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma [PIK3CG], protein kinase D2 [PRKD2], and transforming growth factor beta 3 [TGFB3]. These genes were involved in positive regulation of the acute inflammatory response, positive regulation of vascular endothelial growth factor production, response to estrogen cellular response to growth factor stimulus, regulation of the T cell receptor signaling pathway, and positive regulation of response to DNA damage , during , and after (post-8 cycles) neoadjuvant chemotherapy to compare sequence coverage depth changes around TSSs in 12 responders and 10 nonresponders. The TSS regions of 321 genes . Ayers et al.26 built a 74-gene model classifier to predict pathologic response to neoadjuvant T/FAC therapy, achieving high positive predictive value and specificity.For breast cancer, neoadjuvant chemotherapy is equivalent to postoperative treatment for breast cancer and is used to reduce tumor size, decrease tumor stage, prolong patients’ DFS and OS, and conserve breast tissueAlthough the genes identified in the present study are different from those in previous reports, they belong to several of the same pathways . This may be explained by the fact that different genes play a role in different body parts. We also identified a pathway related to the regulation of erythrocyte differentiation, which may have been due to the presence of peripheral blood cell DNA; its relationship with tumorigenesis is unknown. These results support the feasibility of predicting the efficacy of neoadjuvant chemotherapy prior to treating breast cancer.We examined changes in TSS region coverage in plasma collected from breast tumors at different time points during neoadjuvant chemotherapy as well as their association with response to treatment. Examining changes throughout treatment may provide more information regarding patient responsiveness than analyzing static time points. Analyzing changes throughout treatment may facilitate the development of improved predictors of response and drug resistance.BRCA1, HIC1, and HMGB1 were involved in several of these pathways. BRCA1 is a tumor suppressor gene whose structural and functional abnormalities are closely related to the incidence of breast cancer. It plays an important role in the regulation of cell cycle progression, DNA damage, the repair of cell growth and apoptosis, transcriptional activation and inhibition, and other biological pathways27. HIC1, a tumor suppressor gene, is epigenetically silenced in a variety of tumors, and deleting HIC1 might contribute to premalignant transformation in the early stages of tumor formation28. The HMGB family is a group of chromosomal proteins involved in DNA replication, recombination, transcription, and repair that is related to the progression of a variety of cancers, including colorectal cancer29, hepatocellular carcinoma30, and gastric cancer32. In recent years, an increasing number of studies have focused on changes in gene expression in serial biopsy tissue specimens. Genetic changes associated with prediction and prognosis involve the immune response35, cell proliferation36, apoptosis35, DNA repair24, and the antiinflammatory response26. These findings are consistent with those of the current study.These altered genes were associated mainly with positive regulation of the acute inflammatory response, the TGF-beta signaling pathway, regulation of the T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production Fig. . BRCA1, 37.We also noted significantly altered genes in early treatment (post-1 cycle) in responders. These genes were involved in positive regulation of the acute inflammatory response, positive regulation of vascular endothelial growth factor production, response to estrogen, cellular response to growth factor stimulus, regulation of the T cell receptor signaling pathway, and positive regulation of response to DNA damage. Genes that were significantly altered mid-treatment (post-3/4 cycles) were involved in the PID TGFBR pathway, nonsense-mediated decay, and the establishment of mitotic spindle orientation. These results indicate that different gene changes occurred during chemotherapy treatment. It is also possible that gene expression was delayed for a period after nucleosomes were depleted at the transcription initiation sitescfDNA in plasma comes from apoptotic cells and includes ctDNA released by tumor cells as well as DNA from peripheral blood cells and other tissue. Therefore, gene changes based on cfDNA analyses reflect not only tumor tissue but also the reactions of the blood system and other tissue in the body, such as immune cells. Another limitation of this study is its relatively small sample size. Therefore, we consider our analyses to be exploratory: Larger studies are required to validate our findings and confirm specific associations between molecular data and clinical outcomes.In summary, we confirmed a correlation between the cfDNA nucleosome footprint profile in the region around TSSs and gene expression. We also found significantly different nucleosome footprint profiles in the region near TSSs in plasma cfDNA from healthy individuals versus patients with breast cancer and in plasma cfDNA from responders versus nonresponders before, during, and after a series of neoadjuvant chemotherapy treatment cycles. These genes were related to pathways involved in the inhibition of cell proliferation, response to DNA damage, and immune response. These findings are expected to increase the feasibility of plasma cfDNA nucleosome profiling as a new biomarker for predicting the efficacy of neoadjuvant chemotherapy for breast cancer.2 atmosphere. MDA-MB-231 cells were cultured in DMEM medium (Gibco) supplemented with 20 mM HEPES and 10% FBS at 37 °C in a humidified 5% CO2 atmosphere. Cell supernatant and cell particles were collected over a period of 48 h and subsequently used for high-throughput sequencing.The human breast cancer cell lines T-47D and MDA-MB-231 were obtained from ATCC. T-47D cells were cultured in RPMI-1640 medium  supplemented with 20 mM HEPES and 10% fetal bovine serum (FBS) at 37 °C in a humidified 5% CO38. Clinical characteristics of the patients are presented in Tables The study included 85 healthy individuals from Guangzhou Darui Biotechnology company and 30 from the Third Affiliated Hospital of Southern Medical University, and 145 breast cancer patients from the First People’s Hospital of Foshan in Guangdong, China. Ethical approval for the study was received from the Ethics Committee of the Affiliated Foshan Hospital of Sun Yat-Sen University. All participants provided written informed consent. A total of 115 healthy individuals and 145 breast cancer patients were sampled before any treatment. 38 of 145 breast cancer patients received 24 weeks of sequential epirubicin-cyclophosphamide-docetaxel preoperative chemotherapy followed by resection. Of these 38 patients with neoadjuvant chemotherapy, 22 patients were also sampled at 3 time points during treatment: after the first cycle (post-1 cycle), after the third or fourth cycle , and after the eighth cycle (post-8 cycles). Postsurgical assessment was performed according to the evaluation criteria of RECIST  version 1.0A total of 1 mL peripheral blood was collected in EDTA tubes from each patient and immediately centrifuged for 10 min at 16,000 rpm, 4 °C, and ~500 µL plasma and cell supernatant was stored at −80°C before use, which yielded at least 1 ng total cfDNA for sequencing. cfDNA extraction from plasma and cell supernatant was performed with the QIAamp DNA Blood Mini Kit (Qiagen). We prepared a starting amount of approximately 1–5 ng DNA  for library construction using the Life Sciences Ion Xpress™ Plus Fragment Library Kit, and we omitted the fragmentation step because of the degradation of plasma DNA. The number of PCR cycles was set to 12. Libraries were analyzed on a Bioanalyzer instrument  to observe the DNA size distribution. Sequencing was performed with the Ion PI™ Hi-Q™ OT2 200 Kit and the Ion PI™ Hi-Q™ Sequencing 200 Kit. Ten libraries were pooled together and subjected to 520 flow on the Ion Proton platform , and 6–10 million reads were generated for each cfDNA sample.7 cells were prepared for nucleosome digestion and DNA extraction with the Active Motif Inc Nucleosome Preparation Kit. A total of 50 µL obtained chromatin and 2.5 µL working stock enzyme was incubated for 15 min at 37 °C. The digested nucleosome samples were immediately used for the next step of DNA extraction. Approximately 100 ng DNA was used for library construction and sequencing. Libraries were analyzed on a Bioanalyzer instrument (Agilent Technologies) to observe the DNA size distribution. The kits and parameters used were the same as for cfDNA sequencing. Approximately 100 million reads were generated per sample by MNase-seq.Approximately 107 cell particles using TRIzol Reagent . The amount and quality of the RNA were assessed with a NanoDrop™ 8000 UV Spectrophotometer . We used 1 µg total RNA for mRNA purification using a Dynabeads™ mRNA DIRECT™ Purification Kit (Invitrogen). We prepared the purified product for library construction using the Ion Total RNA-Seq Kit v2. We quantified the concentration of mRNA using the Qubit™ 3.0 Fluorometer (Invitrogen). Experimental operations followed the RNA enrichment and library generation protocols provided in the manual. Two libraries were pooled together and subjected to 520 flow on the Ion Proton platform (ThermoFisher Scientific), and 30–40 million reads per sample were generated.We extracted RNA from approximately 1039. For mRNA sequencing, we aligned sequencing reads with the GENCODE human transcriptome (Release 30) using Salmon (version 0.13.1)40 and used transcripts per million (TPM) to quantify the expression of each gene.For cfDNA sequencing and MNase sequencing, we aligned sequencing reads with the human reference genome (hg19) using TMAP and removed PCR duplicates using the SAMtools (version 1.9) rmdup functionGene information was obtained from RefSeq. For cfDNA sequencing data, we calculated read counts of regions ranging from –1k bp to +1k bp around TSSs using bedtools (version 2.17.0), then normalized them using the reads per kilobase per million mapped reads (RPKM) method to present cfDNA-based nucleosome occupancy. In the MNase sequencing data, only the nucleosome-depleted region  showed depleted coverage, so we used coverage depth of the NDR to quantify the nucleosome occupancy of each TSS. The total depth of each NDR was calculated with SAMtools (version 1.9).p < 0.01 and |log(fold change)| ≥ log1.5 were considered significantly changed. Hierarchical clustering was performed with the average linkage clustering algorithm, and heatmaps were plotted with the pheatmap package (version 1.0.12).Based on the cfDNA RPKM value of each TSS, we performed Wilcoxon rank sum tests (two-sided) to identify TSSs with altered cfDNA coverage between groups. TSSs with To evaluate concordance across the three platforms, we performed Spearman’s rank correlation analyses for gene expression, MNase-based TSS nucleosome occupancy, and cfDNA-based TSS nucleosome occupancy profiles. Moreover, for MNase-seq and RNA-seq data, sequence coverage depth around TSSs was compared between highly expressed genes (TPM > 10) and unexpressed genes (TPM = 0), and the depth of each genomic site was calculated with SAMtools (version 1.9).z scores and used to compute two-sided p values to determine the significance of overlaps.Furthermore, we performed permutation tests to estimate the significance of overlaps between gene expression profiles and TSS profiles . We computed the frequency of highly expressed genes (TPM > 10) with high nucleosome occupancy (genes in the upper quartile), unexpressed genes (TPM = 0) with high nucleosome occupancy, highly expressed genes with low nucleosome occupancy (genes in the last quartile), and unexpressed genes with low nucleosome occupancy. A null distribution was generated from 1,000 permutations. The distributions were then standardized based on http://gepia.cancer-pku.cn)41, and genes with different cfDNA TSS coverage (p < 0.01 and |log[fold change]| ≥ log1.5) between breast cancer patients and healthy donors were selected. Permutation tests were performed to determine the significance of overlaps.Similarly, we estimated the concordance between differentially expressed genes and genes with altered cfDNA TSS coverage in breast cancer patients. We filtered the list of differentially expressed genes using GEPIA .Genes with significant differential TSS coverages were used to develop promoter profiling-based classifiers, and fivefold cross validation was used to randomly divide samples into training and validation sets and evaluate the performance. In the training set, the normalized read count of each TSS was discretized according to the optimal cut-off point before classifier construction. The optimal cut-off point of each promoter was defined as the maximum value of (sensitivity + specificity)/2 in the training sets. R package glmnet (version 2.0–16) was used to perform the least absolute shrinkage and selection operator (LASSO). Receiver operating characteristic (ROC) analysis was used to calculate area under curve (AUC) of the validation set using pROC (version 1.16.2) R package (version 3.5.1). The whole process was repeated 100 times.http://metascape.org)42.We performed functional annotation and enrichment analyses using metascape (Further information on research design is available in the Supplementary informationSupplementary Data filesReporting Summary Checklist