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Transcriptional changes and the role of ONECUT1 in hPSC pancreatic differentiation

Abstract

Cell type specification during pancreatic development is tightly controlled by a transcriptional and epigenetic network. The precise role of most transcription factors, however, has been only described in mice. To convey such concepts to human pancreatic development, alternative model systems such as pancreatic in vitro differentiation of human pluripotent stem cells can be employed. Here, we analyzed stage-specific RNA-, ChIP-, and ATAC-sequencing data to dissect transcriptional and regulatory mechanisms during pancreatic development. Transcriptome and open chromatin maps of pancreatic differentiation from human pluripotent stem cells provide a stage-specific pattern of known pancreatic transcription factors and indicate ONECUT1 as a crucial fate regulator in pancreas progenitors. Moreover, our data suggest that ONECUT1 is also involved in preparing pancreatic progenitors for later endocrine specification. The dissection of the transcriptional and regulatory circuitry revealed an important role for ONECUT1 within such network and will serve as resource to study human development and disease.

Introduction

Damage to the endocrine pancreas can cause several forms of diabetes mellitus. Diabetes, as one of the major diseases in industrial countries, affects over 350 million people worldwide. Type 1 (T1D) and type 2 diabetes (T2D) are the most common forms and have a multifactorial etiology, but the current classification does not cover the large clinical and biological variability of the disease1,2. While T1D is a chronic autoimmune disease affecting insulin-producing β-cells, T2D is a metabolic disease. In T2D, insulin deficiency is caused by insulin resistance in target organs and pancreatic β-cell failure. The onset and pathophysiology of diabetes is influenced by a complex interplay of several factors such as environment, genetic predisposition, and immune system. Additionally, a small subset of diabetes patients, generally estimated to account for approximately 1–5% of cases, is caused by monogenic mutations3,4 altering the development, function or survival of β-cells through a variety of mechanisms. Interestingly, frequent variants of several genes, whose mutations cause monogenic diabetes, are also associated with an increased risk of multifactorial diabetes. Therefore, the characterization of pancreatic differentiation and the study of genes and regulatory pathways involved in pancreatic endocrine development and function are extremely valuable to discover potential candidates contributing to the development of different forms of diabetes.

Binding of transcription factors (TFs) at specific promoters, enhancers, and repressors controls gene expression and thus, drives cellular differentiation. Understanding the transcriptional regulations that underlie cell fate decisions requires the characterization of TF binding sites across multiple differentiation stages. Pancreatic in vitro differentiation of human pluripotent stem cells (PSC) constitutes a suitable human model system with access to distinct developmental stages5,6,7,8,9,10,11,12. In turn, different forms of diabetes have been appropriately modeled by PSC differentiations13. Sequencing techniques such as RNA and Assay for Transposase Accessible Chromatin sequencing (RNA-seq and ATAC-seq) can be applied at distinct differentiation stages to allow the global characterization of transcriptional and regulatory changes during pancreatic differentiation. Moreover, computational analysis of ATAC-seq data such as digital footprinting14,15 can be applied to systematically identify cis-regulatory regions and putative TF binding sites controlling stage-specific regulation. With chromatin immunoprecipitation DNA sequencing (ChIP-seq) the actual binding of specific TFs to such regulatory gene clusters can be determined.

Here, we applied stage-specific RNA-, ChIP-seq, and ATAC-seq experiments during PSC-based differentiations to dissect transcriptional and regulatory mechanisms during pancreatic development. We specifically focused on the functional role of the TF ONECUT1 and found an important role as regulator of pancreas progenitor differentiation in our analysis. In our accompanying study, we could additionally demonstrate that mutations in ONECUT1 contribute to a broad spectrum of diabetes16.

Results

Transcriptome maps of pancreas differentiation from human pluripotent stem cells

Notably, human pancreatic development is faithfully recapitulated by differentiation of PSCs into the pancreatic lineage5,6,7,11,17. We acquired stage-specific RNA-seq, ATAC-seq, and ChIP-seq data in such a human stem cell-based differentiation approach to characterize pancreatic development (Fig. 1a). Stage-specific modulation of signaling pathways such as Wnt and Hedgehog differentiates PSCs from embryonic stem cell stage (ESC) towards definitive endoderm (DE), followed by gut tube endoderm (GT), pancreatic endoderm (PE), and pancreatic progenitors (PP). Key TFs and cellular markers were indeed stage-specifically expressed (Fig. 1b, c and Supplementary Fig. 1a, b). SOX2 (cluster I), an essential factor for pluripotency and self-renewal18 was expressed in pluripotent stem cells and became downregulated during DE differentiation, while in exchange markers of DE stage such as GATA6, CXCR4, and SOX1719,20,21 became upregulated (cluster II). Expression of FOXA1/2, important for pancreatic specification by regulating PDX1 expression22,23, was induced during DE stage (cluster III) and its expression was sustained in PE stage. In turn, PDX1 was upregulated at PE stage (cluster IV). Similarly, GATA4 was expressed at PE stage and NKX6.1, GP2, PROX1, PTF1A, and SOX9 at PP stage (cluster III, IV)24,25,26. Moreover, TFs important for later stages of endocrine development such as GLIS3, NEUROD1 and ISL1 (cluster IV, VII) started to be expressed at PE and PP stage27,28. To support these gene expression dynamics during pancreatic differentiation, we employed two additional human embryonic stem cell lines (Fig. 1d–f and Supplementary Fig. 1c, d). We observed a similar expression pattern of stage-specific TFs in two further data sets employing distinct cell lines5,17,29,30, where key markers for PSC (SOX2), DE (SOX17, CXCR4), PE (PDX1), and PP stage (NKX6.1) as well as other TFs such as FOXA2, SOX9, and PROX1 were expressed at the respective stages. To evaluate the capacity of the protocols and to quantify stages closer to pancreatic endocrine cells, we performed a gene set-based analysis with developmental genes of the endocrine pancreas. The expression of endocrine genes was enriched in PE and PP stage for HUES89,11,12 and MEL1 protocols17 and for PP and endocrine progenitor (EP) stage in the CyT49 protocol29 (Fig. 1g). Expression of endocrine genes was again decreased in CyT49 differentiation stages resembling fetal β-cells (FE stage). Moreover, the HUES8 protocol explored herein had the highest increase of pancreatic endocrine gene expression among all three protocols.

a Schematic outline of applied pancreatic differentiation strategy of human pluripotent stem cells and subsequent stage-specific large-scale sequencing analysis. RNA-seq data (own and E-MTAB-1086), ATAC-seq data (own), ChIP-seq data of ONECUT1 (own), FOXA1, FOXA2, PDX1 (GSE54471, GSE149148) and NKX6.1 (own), and histone data (H3K4me1, H3K27ac, GSE54471; H3K4me3, ArrayExpress E-MTAB-1086) were used for analysis. b Stage-specific expression analysis of depicted genes during pancreas differentiation of HUES8 cells (RNA-seq, n = 6 biologically independent samples). c Fuzzy c-means (FCM) clustering of the differentially expressed genes (RNA-seq) at the varying differentiation stages of hESCs (HUES8) displaying selected genes at respective clusters. d Stage-specific expression analysis of depicted genes during pancreas differentiation of CyT49 cells30 (RNA-seq, n = 2 biologically independent samples). e Heatmap of fuzzy c-means clustering of differentially expressed genes (RNA-seq) at distinct pancreatic differentiation stages of CyT49 cells30. Selected genes of particular clusters are highlighted. f Heatmap of fuzzy c-means clustering of differentially expressed genes (RNA-seq, n = 4) at the distinct pancreas differentiation stages of MEL1. Selected genes of particular clusters are displayed. Of note, cells at PP stage were purified for PDX1 and NKX6.1. g Expression of genes associated with endocrine pancreas differentiation obtained from Gene Ontology (ID GO:0031018) for all protocols. Boxplots depict median (center), interquartile range (box) and extreme values (whiskers). A statistical test (Wilcoxon Rank Sum test; one sided) was used to compare the expression of endocrine genes vs. all other genes.

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Open chromatin maps of pancreas differentiation from human pluripotent stem cells

To spatiotemporally map genome-wide chromatin accessibility during pancreatic differentiation, we performed the ATAC with massively parallel sequencing at different stages of pancreatic differentiation (Fig. 2a). First, clustering of the developmentally regulated open chromatin (OC) regions revealed stage-specific OC clusters like clusters obtained via RNA-seq (Figs. 1c and 2a). The SOX2 locus (cluster I) was accessible only in ESC stage, whereas the loci for CXCR4 and SOX17 (cluster IX) became accessible during differentiation to DE stage. The FOXA1/2 locus had limited accessibility at DE stage but opened during further development (cluster VI, VIII) like RNA expression patterns. The NKX6.1 locus was allocated to a cluster peaking at the PP stage (Cluster V; Fig. 2a). Similarly, chromatin accessibility for other core endocrine gene loci such as GLIS3 or MAFB was initiated from PE to PP stage (Cluster VIII; Fig. 2a).

a Fuzzy c-means clustering of the differential open chromatin peaks (ATAC-seq) during pancreatic differentiation of HUES8 cells. b TF binding motif enrichment at distinct open chromatin clusters of ATAC-seq. c Average chromatin accessibility profiles around footprint-supported bindings sites of selected TFs. d Heatmap ChIP-seq signals (+/− 5 kb of peak center) of H3K4me1, H3K4me3, and H3K27ac at ATAC-seq peaks from cluster PE-PP (VIII; clustering is shown in Fig. 2a). Peaks are ordered by the decrease in the H3K4me1 mark at PE stage. We also depict ONECUT1 ChIP-seq signals (PP stage) in the vicinity of the peak (+/− 5 kb).

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Next, we investigated if TF binding motifs were stage-specifically enriched in OC loci to dissect regulatory changes between distinct TFs (Fig. 2b). This unbiased footprinting analysis indicated the activity of well-known TFs to control pancreas expression programs: GATA6 and SOX9 in early stages and NKX6.1, NKX6.2, FOXA1, FOXA2, MAF, PDX1, and ONECUT1 in later stages. This cell-specific enrichment, i.e., importance of GATA6 in early DE stage, FOXA2 in PE stage and ONECUT1 in both PE and PP stages, was illustrated by the accessibility profiles around footprinting-supported motifs (Fig. 2c). This showed an increasing activity of ONECUT1 motif with a peak on both PE and PP stages, while FOXA2 had a peak activity in PE stage only.

ONECUT1 during human pancreas development

Following our genetic study identifying ONECUT1 as diabetes gene involved in a broad spectrum of diabetes in human (accompanying article)16, we focused our further analysis on ONECUT1. This TF has so far been mainly characterized in mouse models31,32,33,34 and little is known about its role during human pancreatic differentiation35. ONECUT1 expression increased from the DE stage to the PE stage with sustained expression at the PP stage (Fig. 1b, f). ONECUT1 was assigned alongside key TFs necessary for PE development including FOXA1/2, GATA4, as well as NGN3 and MNX1 known to be important for the initiation of the β-cell program (Cluster III; Fig. 1c). Robust expression from the PE to the PP stage was reproduced on published data sets in several genetic backgrounds (MEL15,17 and CyT4929,30 hESCs) (Fig. 1g–j). Notably, ONECUT1 was only marginally expressed at the gut tube stage (Fig. 1f). Moreover, the ONECUT1 locus opened around the GT and peaked at the PE stage (Fig. 2a), closely correlating with gene transcription levels (Figs. 1b, c). Additionally, motif enrichment and footprint analysis15 confirmed the dynamic and increasing activity of ONECUT1 from the PE to the PP stage (Fig. 2b, c). While ONECUT1-bound elements within the PE/PP cluster had high levels of active enhancer marks (both H3K4me1 and H3K27ac) beginning at the PE stage, they displayed very low levels of the promoter mark H3K4me3 at all stages (Fig. 2d). Taken together, these data suggest that accessible chromatin dynamics during pancreatic cell fate acquisition follow precise stage-specific patterns.

ONECUT1 is a crucial regulator of pancreas progenitors

We have recently investigated the occupancy of ONECUT1 binding at the GT and PP stages by performing ChIP-seq experiments35. In line with the ONECUT1 expression profile, we observed only a few hundred ONECUT1 ChIP-seq peaks at the GT stage, predominantly at promoter sites. In contrast, at the PP stage tens of thousands of ONECUT1 ChIP-seq peaks were found, predominantly located in distal gene regions (Fig. 3a). ONECUT1-bound genes were enriched within the PE, PE-PP transition, and most abundantly in the PP gene cluster, indicating an increasingly important role of ONECUT1 during PP differentiation (Fig. 3b, c). Similar enrichment of ONECUT1 in PE and PP clusters was also observed in cells from other genetic backgrounds (Fig. 3f, g). GREAT analysis (Genomic Regions Enrichment of Annotations Tool36) of ONECUT1-bound genes was associated with processes such as (endocrine) pancreas and endocrine system development, decreased insulin secretion, and pancreatic hypoplasia (Fig. 3d, e).

a Distribution of ONECUT1 ChIP-seq peaks at GT and PP stages relative to genomic regions. b Schematic of the binding enrichment analysis evaluating whether ONECUT1-bound genes (binding observed 20 kb upstream or downstream of the respective promoter regions) are enriched in particular RNA-seq clusters. c Binding enrichment (z-score) test of ONECUT1 (ChIP-seq, PP stage) in genes of the RNA-seq clusters of HUES8 cells (z-test; one-sided). d, e Enrichment analysis (GREAT36) of ONECUT1-bound genes (ChIP-seq, PP stage) (Fischer Exact test; one-sided). f Binding enrichment (z-score) test of ONECUT1 (ChIP-seq, PP stage) in genes of the RNA-seq clusters of CyT49 cells (z-test; one-sided). Significance is indicated above bars (Fischer Exact test; one-sided). g Binding enrichment test of ONECUT1 (ChIP-seq, PP) for MEL1 RNA-seq specific gene clusters. Data show binding score (z-score) with p-value (z-test; one-sided). h Overlap of ONECUT1 ChIP-seq peaks (PP stage, own data set) with peaks from other TFs (previously published at GSE54471, GSE149148, or NKX6.1; own data set). For the other TFs, only the number of binding peaks overlapping with ONECUT1 is shown.

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During pancreatic cell differentiation, the state of enhancers is accompanied by a defined sequence of chromatin changes: chromatin decompaction via pioneer factors or cooperative TF binding, poising/priming via accumulation of H3K4me1 marks, and activation by deposition of H3K27ac marks. This leads to the acquisition of a cell fate-specific gene expression program29,37,38,39,40,41,42. To further characterize the nature of distal ONECUT1 binding sites at the PP stage (Fig. 3a), we cross-referenced our data with public and own ChIP-seq data for enhancer histone marks (active: H3K4me1/H3K27ac; poised: H3K4me1) and TFs (NKX6.1, FOXA2, GATA6 and PDX1) at the PP stage29,30. To characterize chromatin states associated with ONECUT1 and these TFs, we made use of a chromHMM annotation data set43 employing the same pancreas differentiation stages35. This annotation allowed us to gather additional information of active and inactive enhancers, active promoters and repressive regions among others.

First, we observed an overlap of ONECUT1 peaks with PDX1, GATA6, FOXA2, and NKX6.1 in 21, 10, 6, and 4% of peaks (Fig. 3h). The evaluation of chromatin states indicated that ONECUT1 primarily bound to enhancers (strong or weak) and the number of enhancers particularly increased after the GT stage. ONECUT1 was bound to 3.6% of active enhancers existing in GT stage, 9.7% of active enhancers in PE stage and 14% of active enhancers in PP stage (Fig. 4a). Among TFs analyzed here, only PDX1 was bound to a larger number of active enhancers, e.g., 9%, 21%, and 31% for GT, PE, and PP stages, respectively (Fig. 4a). Next, we asked if co-binding of GATA6, PDX1, NKX6.1, and FOXA2 with ONECUT1 is enriched for particular chromatin states. Interestingly, we observed that co-binding events of ONECUT1 with these TFs were particularly enriched in active enhancers (Fig. 4b). Highest increase (log2 fold change >2) was detected in PE and PP stages for FOXA2, PDX1 and GATA6. To check if these co-binding events were reflected in motif positioning, we performed a MEME-ChIP analysis. This analysis detected a de novo ONECUT1 motif (89% of peaks) central to peaks and a FOXA2 motif (24% of peaks), which preferentially binds 9–10 bps from the peak center (Fig. 4c). This also supports a physical proximity and possible interaction of ONECUT1 and FOXA2. These results support altogether that ONECUT1 is associated to activation of PE and PP specific enhancers by presumably  co-binding PDX1, GATA6 and FOXA2.

a Overlap of peaks of distinct TFs on chromatin states as delineated by chromHMM. These states include active enhancers (EnhA), weak/poised enhancers (EnhWk.), repressed regions (Repr.), active promoters (TssA), poised promoters (TssBiv) and regions flanking active promoters (TssFlnk1-2). b Log2 fold change (FC) of the frequency of ONECUT1 co-binding events (ONECUT1 with either GATA6, PDX1, NKX6.1, and FOXA2) vs. ONECUT1 exclusive binding sites for distinct chromatin states and differentiation stages. Red values indicate chromatin stages over-represented in co-binding events. We highlight values with abs(log2(FC)) > 2. c We show MEME de novo motifs around ONECUT1 ChIP-seq peaks, which are found in 89% of peaks, and FOXA2 motif, which is found in 24% of peaks. Both motifs have a preference relative to the peak center, i.e., ONECUT1 is mostly localized in the peak middle, while FOXA2 is located 9–10 bps downstream of peak center. d IGV Genome browser view of ONECUT1 and PE/PP expressed genes with ChIP-seq and ATAC-seq profiles from GT, PE, and PP stages. We highlight ONECUT1 peaks overlapping with active enhancers (blue tracks below H3K27ac tracks) gained at PE or PP stage, as well as co-binding partners.

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The previous analysis indicated that ONECUT1 is associated with novel active enhancers in PE and PP stages. We therefore investigated genomic loci of genes with PE and PP specific expression (Fig. 1c, e), which gained an active enhancer from GT to PE or PE to PP stages. The ONECUT1 gene has a large PP specific de novo active enhancer up-stream of its promoter, which was co-bound by ONECUT1, FOXA2, GATA6, and PDX1 (Fig. 4d). FOXA2 and NGN3 (NEUROG3) were two genes with high expression in PE/PP stages (Fig. 1c). Both had ONECUT1 binding sites associated with novel acquisition of an active enhancer at the PE stage by either co-binding with GATA6 or PDX1 (Fig. 4d). PROX1, GP2, and SOX9 were genes with high expression in PP stage (Fig. 1c, e). These genes had several de novo active enhancers mostly gained at PP stage (Fig. 4d). Notably, ONECUT1 co-binds with GATA6 and FOXA2 in most of these enhancers. In all enhancers, we observed the deposition of H3K4me1 marks and ATAC-seq starting from GT stages and preceding the activating marks H3K27ac. This supports the priming mechanism of ONECUT1 observed in Fig. 2d and provides examples of important pancreas genes being regulated by ONECUT1.

ONECUT1 regulates islet cell genes

Subsequent GREAT analysis highlighted the role of ONECUT1 in specification toward a β-cell-specific program with top-ranked terms like “MODY” or “regulation of β-cell development” (Fig. 5a, b). ONECUT1-bound pancreatic enhancers accumulated H3K4me1 around the GT stage, while H3K27ac accumulated only from GT stage on, surpassing H3K4me1 levels at the PP stage (Fig. 5c). This is in line with the observation that pancreatic enhancers acquired a poised state around the GT stage prior to activation29 and indicates that ONECUT1 might be crucial for priming and activating enhancers formed at the PE stage.

a, b Enrichment analysis (GREAT36) of ONECUT1-bound pancreatic enhancers (FG/PE and PE clusters from ref. 29) for “pathway terms” (a) from the MSigDB pathway genes subset CP and for “GO Biological Process” (b) from the Gene Ontology Consortium (Binomial test; one side). c Distribution of stage-specific H3K4me1 and H3K27ac ChIP-seq signal at ONECUT1-bound pancreatic enhancers (FG/PE and PE clusters from ref. 29). Boxplots depict median (center), interquartile range (box) and extreme values (whiskers), point and connecting line mean value. d Binding enrichment (z-score) test of ONECUT1 (ChIP-seq, PP stage) in distinct OC clusters of HUES8 ATAC-seq (z-test; one side). e Enrichment analysis (GREAT36) of ONECUT1-bound genes overlapping with PE/PP ATAC-seq (cluster VIII) for “pathway terms” (Binomial test; one side). f, g Overlap between ChIP-seq peaks of ONECUT1 (PP stage) and depicted TFs with islet enhancers (f) and islet promoters (g) together with distinct histone modification as defined by ref. 45. h Overlap between ONECUT1 ChIP-seq peaks (PP stage) with islet TF peaks (NKX6.1 and NKX2.2 ChIP-seq from human islets45; intersection test, one side84). i Interactive Genome Viewer (IGV) plots illustrates ONECUT1 binding peaks and histone marks in promoter regions for selected endocrine genes. Genomic locations refer to human genome assembly GRCh37 (Hg19).

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To correlate ONECUT1 binding with accessible chromatin, we mapped ONECUT1 ChIP-seq binding peaks from the PP stage to OC clusters from ATAC-seq experiments. ONECUT1 binding was most evident in the transition cluster VIII (PE-PP), followed by cluster V (PP), which indicates that ONECUT1 binding might start at the PE stage to promote a PP program (Fig. 5d). GREAT analysis of the PE-PP cluster identified “MODY” and “regulation of β-cell development” top-ranked together with “FOXA transcription” and “RXR/RAR heterodimerization” (Fig. 5e). Of note, it has been reported that retinoic acid signaling is crucial for establishing the PP stage44. These results indicate that ONECUT1 is bound to active enhancers starting to be accessible at the PE and/or PP stages, supporting its central regulatory function within the pancreatic transcriptional network.

Moreover, ONECUT1 bound to active enhancers (7%) and promoters (18%) (Fig. 5f, g) and to regions bound by NKX6.1 (13%) and NKX2.2 (9%) in human islets45 (Fig. 5h). Interestingly, a large proportion of the few PP ONECUT1 promoter-bound sites were proximal to genes expressed in islets (Fig. 5g), which included promoters of key genes such as RFX3, RFX6, KCNK3, and MAFB active in β-cells (Fig. 5i). In summary, ONECUT1 expression is sustained from the PE to the PP stage and the protein binds to active pancreas-specific enhancers as well as active regulatory regions of islet cells.

Discussion

Here, we comprehensively characterized transcriptional and chromatin changes in pancreatic in vitro differentiation. Our global resource consists of transcriptional (RNA-seq), chromatin (ATAC-seq) and regulatory (ONECUT1 ChIP-seq) analyses at distinct pancreas differentiation stages. These experiments were complemented with our own and publicly available ChIP-seq data measuring histone marks (H3K4me3, H3K4me1, H3K27ac) and TF (FOXA2, GATA6, NKX6.1, and PDX1) binding at the PP stage. Computational analysis of transcriptional and OC data in our human stem cell differentiation approach revealed expression of ONECUT1 during early pancreatic development at PE and PP stage together with key pancreatic TFs consistent with data from mouse studies32,33,46. Our protocol for differentiating HUES8 cells towards the pancreatic lineage indicated robust induction of the endocrine gene expression program at PE and PP stage. This suggests that our protocol is suitable to explore early transcriptional regulators as well as the impact of so far unknown gene variants impairing endocrine development in diseases such as diabetes. Other cell lines revealed similar but slightly different patterns. Depending on genes to be explored and e.g., the availability of certain reporter cell lines, it might be beneficial to follow gene expression in different protocols and cell lines.

Moreover, ONECUT1-bound genes were enriched for pancreatic and endocrine development. This is in line with Onecut1 knock-out mouse data, demonstrating that Onecut1 regulates Pdx-1 and Ngn3, which are important for pancreatic endocrine specification32,33,46. Onecut1 and Pdx1 interact during murine endocrine development and the combined function of both transcription factors is necessary for β-cell maturation and adaptation46,47,48. This correlates with observed protein interaction16 as well as our motif analysis revealing co-binding to active enhancers during human pancreas differentiation. Our chromatin-regulatory and cis-regulatory binding maps provide evidence that gene transcription regulated by ONECUT1 is located in clusters of OC, which are co-bound within enhancer regions by a complete suite of physically interacting TFs normally enriched in islets45,49,50. Our analysis further suggests combined priming activity of ONECUT1 with TFs FOXA2, GATA6, PDX1, and NKX6.1 by preparing and activating enhancers of important pancreatic regulators for expression. Previously, the major pancreas TF PDX1 was found to regulate β-cell development by binding mainly intron and intergenic but also promoter regions of pancreatic genes such as RFX6, HNF1B, and MEIS151. Our analysis further suggests a role of ONECUT1 priming activity for preparing loci of important pancreatic regulators for expression, e.g., NGN3 (NEUROG3) important for generation of functional β-cells52. Similarly, SOX9, important for regulating NGN3 as well as other TFs during endocrine development53, and PROX1, playing a role in pancreatic progenitor specification and proliferation as well as insulin secretion26,54, are regulated by this TF network and therefore contribute to timed development. This is in line with data gathered in our recent study indicating that binding of ONECUT1 at enhancers triggers an active state in PPs to allow expression of islet-specific TFs NKX6.1, NKX6.2, and NKX2.216. Similar binding to regulatory regions of genes important for endocrine cell development was observed for Nkx6.1 in mice55,56. This may suggest that ONECUT1 plays a similar role inducing the pancreatic transcriptional program and priming cells for further endocrine differentiation.

Binding of ONECUT1 to islet enhancers included regions of genes encoding proteins such as INSM1, important for maturation of endocrine precursor cells and maintenance of adult pancreatic β‐cells57,58 and FOXA2, a regulator for pancreas development and adult pancreatic β‐cell function59,60. In addition, we also observed ONECUT1 binding to active promoters of genes important for β-cell development and function. RFX6 coordinates islet development and β-cell identity in human and mouse but also controls the number of PPs52,61,62. Similarly, we found promoter binding to RFX3, important for differentiation and function of β-cells in mice63. We also found ONECUT1 binding to the promoter of the highly expressed potassium channel KCNK3 (also known as TASK-1) involved in modulating insulin secretion and glucose homeostasis64, as well as in the promoter of MAFB, which is required for regulating development of α- and β-cells in human and in mice65,66. Altogether, our global analyses establish ONECUT1 within the interconnected and tightly controlled regulatory network of pancreatic TFs during pancreatic and endocrine development. Although our data suggest a role for ONECUT1 in pancreatic and endocrine development, limitations apply to bulk cell analysis of differentiation stages due to the complex heterogeneity and interconnectedness of cell populations. Bulk sequencing data illustrate an average characteristic of cell populations and are not able to resolve the complex composition and multiple pathways at a specific differentiation stage. In order to address a possible bias, further in-depth analysis of single-cell data will be needed.

We recently reported ONECUT1 as a gene involved in various forms of human diabetes, ranging from severe neonatal syndromic diabetes (biallelic mutations) and young-onset non-autoimmune diabetes with incomplete penetrance (monoallelic mutations) to common T2D (regulatory variants)16. Further analysis in our human stem cell differentiation model provides evidence that pathogenic ONECUT1 variants disturb the development of pancreatic progenitors and β-like cells due to their diminished capability to activate factors such as NKX6.1, NKX6.2, and NKX2.2 important for β-cell formation.

The present study provides further understanding on the role of ONECUT1 within a tightly controlled network of genes involved in pancreatic and endocrine development. Remarkably, several of the genes belonging to this network are also involved in monogenic and in multifactorial diabetes67,68 further supporting the relevance of our human stem cell differentiation model to help identify and study genes and variants involved in diabetes.

Methods

Stem cell culture

Permission for culture and pancreatic differentiation of human embryonic stem cell (hESC) lines was obtained from the Robert Koch Institute within the “79. Genehmigung nach dem Stammzellgesetz, AZ 3.04.02/0084”. HUES8 cell line was received from Harvard University and MEL1 from Stem Cells Ltd. Cells were cultured on plates coated with hESC Matrigel in mTESR1 (STEMCELL Technologies) medium at 5% CO2, 5% O2, and 37 °C and change of medium was performed daily. For splitting our feeder-free single cell cultures twice a week, cells washed with PBS were dissociated for 3–5 min at 37 °C with TrypLE Express (Invitrogen) followed by diluting with blank medium to stop enzyme reaction. Cell suspension was centrifuged at 800 rpm for 5 min and cell pellet was resuspended in mTESR1 medium supplemented with 10 µM ROCK inhibitor (Abcam). Mel1 INSGFP/W cells were cultured according to Nair et al.17.

Differentiation of PSCs into pancreatic progenitor cells

Pancreatic differentiation is based on published protocols69,70. Basal media BE1: MCDB131 (Invitrogen) with 0.8 g/l glucose (Sigma), 1.174 g/l sodium bicarbonate (Sigma), 0.5% fatty acid free BSA (Proliant), 2 mM l-Glutamine; BE3: MCDB131 with 3.32 g/l glucose, 1.754 g/l sodium bicarbonate, 2% FAF-BSA, 2 mM l-Glutamine, 44 mg/l l-Ascorbic acid, 0.5% ITS-X.

For differentiation, culture plates were coated with growth factor reduced Matrigel (BD, 354230) and 300,000 hESCs per 24-well were seeded in mTESR1 containing 10 µM ROCK inhibitor. The following day, differentiation was initiated when cells reached 80% confluence and culture was transferred in a 5% CO2 incubator at 37 °C with daily medium change. After washing with PBS (Sigma), cells received for 24 h BE1 medium with 2 µM CHIR99021 (Axon MedChem) and 100 ng/ml Activin A (R&D). The following 2 days, cells were cultured in BE1 supplemented with 100 ng/ml Activin A and 5 ng/ml bFGF (R&D). At DE stage, cells were maintained for three days in BE1 with 50 ng/ml FGF10 (R&D), 0.75 µM Dorsomorphin (Sigma) and 3 ng/ml Wnt3a (Peprotech) in BE1 followed by BE3 with 0.25 µM SANT-1 (Sigma), 200 nM LDN-193189 (Sigma), 2 µM Retinoic acid (Sigma), and 50 ng/ml FGF10 for three days. From day 9 to 13, cells were maintained in BE3 containing 100 ng/ml EGF (R&D), 200 nM LDN, 330 nM Indolactam V (Stem Cell Technologies) and 10 mM Nicotinamide (Sigma). MEL1 INSGFP/W were differentiated as described in the protocol published by Nair et al.17.

Immunofluorescence staining

Human ESCs differentiated on µ-Plate 24-wells (Ibidi) were used for in-well immunofluorescence staining. Cells were washed with PBS followed by fixation in 4% PFA solution for 20 min at RT. Subsequently, fixed cells were washed three times with PBS and background staining was quenched with 50 mM NH4Cl for 10 min. After washing with PBS, cells were incubated in PBS containing 0.1% Triton-X and 5% normal donkey serum (blocking) at room temperature for 45 min and subsequently, in blocking solution with primary antibodies overnight at 4 °C. Cells were washed twice with PBS containing 0.1% Triton-X and 2% normal donkey serum (wash solution) followed by incubation in blocking solution containing secondary antibodies at room temperature for 1.5 h. Finally, cells were washed with PBS and nuclei were stained with 500 ng/ml DAPI. Images were acquired on a Keyence Biozero BZ-9000 microscope. Antibodies used: OCT3/4 (Santa Cruz; sc-5279; 1:200), NANOG (Cell Signaling; #3580; 1:100), SOX17 (R&D; AF1924; 1:500), PDX1 (R&D; AF2419; 1:500), NKX6.1 (DSHB; F55A10 concentrate; 1:100) in combination with Alexa-conjugated secondary antibodies from Invitrogen.

Immunofluorescence staining on paraffin tissue sections

Spheres derived from MEL1 were fixed for 15 min at RT with 4% paraformaldehyde, washed in PBS and subsequently embedded in 2% agarose (Sigma). Samples were dehydrated and paraffin embedded, followed by sectioning at 5 μm thickness. For staining, rehydrated sections were treated with antigen retrieval solution (Biogenex), blocked (CAS-Block, Life Technologies with 0.2% Triton-X 100, Sigma) and incubated in primary antibodies at 4 °C overnight. After washing in PBS with 0.1% Tween20, sections were incubated with secondary antibodies for 45 min at room temperature. Finally, slides were washed in PBS-T and PBS and mounted with Vectashield. Images were acquired using a Zeiss ApoTome. The following antibodies were used: SOX17 (R&D; AF1924; 1:500), PDX1 (R&D; AF2419; 1:500), NKX6.1 (DSHB; F55A12 concentrate; 1:150), and Alexa-conjugated secondary antibodies from Invitrogen. Nuclei were visualized with DAPI.

Flow cytometry

Markers c-Kit (CD117; APC conjugated; Invitrogen; CD11705; 1:100), CXCR4 (CD184; PE conjugated; Life Technologies; MHCXCR404; 1:33), SOX17 (Alexa488 conjugated; BD Biosciences; 562205; 1:100) and FOXA2 (PE conjugated; BD Biosciences; 561589; 1:100) were quantified for definitive endoderm (DE), PDX1 (PE conjugated; BD; 562161; 1:35) and NKX6.1 (Alexa647 conjugated; BD; 563338; 1:35) were analyzed for pancreatic endoderm (PE) and pancreatic progenitor (PP) using BD LSC II flow cytometer or BD FACSAria II cell sorter with FACSDiva software version 8.0.1 (BD Biosciences) and FlowJo 10.5.0 as described by Philippi et al.16.

RNA sequencing

Total RNA was isolated from different stages of pancreatic cells using GeneJET RNA Purification Kit (Thermo Fisher Scientific) following the manufacturer’s protocol. After quality check, up to 1 µg of total RNA with RNA integrity values (RIN) > 8 was used for poly-A enrichment followed by library preparation using the TrueSeq stranded mRNA Kit from Illumina (HUES8 n = 6, Mel1 n = 4). For HUES8, a HiSeq 3000 system (Illumina, single read, 1 × 50 bp) at the Biomedical Sequencing Facility (BSF) of the CeMM in Vienna, Austria, was used, whereas for Mel1 RNA samples a HiSeq 2500 system (Illumina, single read, 1 × 50 bp) was utilized for RNA sequencing at the Leibnitz Institute in Jena, Germany.

We used STAR aligner (Version 2.5.2b;71) to align reads on human genome hg38 with parameters as in ENCODE project. Ensembl Transcriptome Annotation e87 was used as transcriptome reference. Normalization and differential expression (DE) was performed with DESeq2 (version 1.22.1)72. We only considered further genes with more than 10 reads, an adjusted p-value < 0.05 and abs(FC) > 1. We next performed clustering of DE genes with a fuzzy c-means algorithm (R package e1071). The same approach was used for HUES8 (ESC, DE, PE, and PP stages on WT), MEL1 (ES, DE, PE, and PP), and CyT49 cells (publicly available at E-MTAB-108630).

We performed functional analysis with gene set enrichment analysis (GSEA version 3.073) to analyze genes. For this, we used the log2-fold change (FC) and the GSEAPreranked function with 1000 permutations in default settings. We manually derived genes sets from Cebola et al.22, which contains 500 pancreatic multipotent progenitor cell (MPC)-specific genes, and from Hrvatin et al.74 for endocrine development genes. We used ToppFun for Gene Ontology enrichment (FDR correction, adjusted p-value cut off at 0.5)75.

ATAC sequencing

For ATAC sequencing, differentiated pancreatic cells were harvested and 50,000 cells were directly lysed in tagmentation buffer containing TDE1 Tagment DNA enzyme, Digitonin and protease inhibitor cocktail (Nextera DNA Library Preparation Kit, Illumina). Lysates were incubated for 30 min at 37 °C and subsequently purified with the MicroElute Kit (Qiagen) according to the manufacturer’s protocol. Samples (n = 3) were stored at −20 °C until further processing and analysis at BSF in Vienna.

ChIP sequencing

The ChIP-IT High-Sensitivity kit (Active Motif) was utilized following the manufacturer’s protocol. Briefly, aggregates of pancreatic cells containing approximately 107 cells fixed for 15 min in an 11.1% formaldehyde solution were applied to chromatin extraction. Cells were lysed in a Dounce homogenizer and DNA was sheared with a Bioruptor®Plus (Diagenode) sonicator, on high for 3 × 5 min (30 s on, 30 s off). For subsequent immunoprecipitation, 10–30 μg of sheared chromatin was mixed with 4 μg primary antibody (HNF6 H-100, sc-13050, Santa Cruz; or NKX6.1 RES310, AB2024, Beta Cell Biology Consortium) and incubated over night at 4 °C on an end-to-end rotator. The chromatin-antibody solution was incubated with Protein G agarose beads for 3 h at 4 °C on the rotator and subsequently samples were processed according to the ChIP-IT High-Sensitivity instructions with an incubation at 65 °C for 2 h to reverse crosslinks and purify DNA. KAPA DNA Library Preparation Kits for Illumina® (Kapa Biosystems) were used to construct DNA libraries. For library sequencing, a HiSeq 4000 System (Illumina®) with single-end reads of 50 bp in the Institute for Genomic Medicine (IGM) core research facility at the University of California at San Diego (UCSD) was used. Additional details on the employed data sets are reported also in refs. 35,59

Bioinformatics analysis of ATAC-seq and ChIP-seq

First, we trimmed reads with skewer (Version 0.2.1;76) and next used Bowtie2 (Version 2.3.4.2;77) to align the human hg19 genome. We only considered properly mapped read pairs in ATAC-seq data. Also, we removed duplicated reads. Peak calling was performed with MACS2 (2.1.2;78) with FDR of 5%. This allowed us to find stage-specific peaks by using corresponding input DNA and replicates. For the ChIP-seq data of TFs (ONECUT1, FOXA1, FOXA2, PDX1, and NKX6.129,35), we also detected de novo motifs with MEME-ChIP (Version 4.12;79). The previous methods were also applied to publicly available histone data (H3K4me1, H3K27ac, GSE5447129 and H3K4me3, ArrayExpress E-MTAB-108630).

Only for ATAC-seq data, we also used a differential peak calling approach (THOR—Version 0.11.6;80) to detect differential peaks between consecutive steps (ESC vs. DE, DE vs. GT, GT vs. PE, and PE vs. PP). We only considered peaks with p-value < 10−5. Next, we built a peak vs. condition count matrix by merging all peaks with bedtools81 and quantification of the number of reads per peak. This matrix was first normalized by library size followed by a z-transformation (removal of mean and scaling to unit variance). Next, we grouped the peaks by clustering with fuzzy c-medoids. Pearson correlation was used as similarity metric.

We performed a footprinting analysis to dissect the regulatory program driving distinct differentiation stages. For this, we use HINT-ATAC (Version 0.11.8;14) to detect footprints at each condition (ESC, DE, GT, PE, and PP). Next, we characterized TFs associated to the footprints with motif matching with Regulatory Genomics Toolbox (RGT; www.regulatory-genomics.org/rgt). JASPAR 201882 was used as a TF motif database. Next, we performed differential footprinting analysis comparing subsequent differentiation steps (ESC vs. DE, DE vs. GT, GT vs. PE, and PE vs. PP) and only considering TFs with significant changes (p-value < 0.05).

Histone and ATAC-seq heatmaps were performed with Deeptools83. We used RGT (RGT; www.regulatory-genomics.org/rgt) to perform a binding enrichment test comparing gene sets with peaks. This is based on a z-score test, which tests if the number of genes close to a peak (tss +/− 20 kb) is higher than in random peaks (1000 randomizations). An intersection test84 was used to assess the overlap between two peak sets. p-values were adjusted for multiple test correction using the Bonferroni method.

Statistics and reproducibility

For RNA-seq and ATAC-seq, at least three independent experiments were performed with the number of biological replicates mentioned in figure legend. Statistical analyses were performed in R (version 4.0.2) or by the software described for each analysis test. The statistical method used for each individual analysis are described in the corresponding figure. All reported p-values based on multi-comparison tests were corrected using the Bonferroni method.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All own sequencing data (RNA-seq, ATAC-seq, and ChIP-seq) and pre-processed files (count matrices, peaks, and genomic profiles) are deposited at Gene Expression Omnibus (GSE167606). We also provide all genomic tracks and genomic regions (peaks) from both own and public data in Zenodo (https://doi.org/10.5281/zenodo.4559829). Public available data include histome ChIP-seq (H3K4me1, H3K27ac, GSE5447129, and H3K4me3, E-MTAB-108630) and transcription factor ChIP-seq (FOXA1, FOXA2; GSE5447129; and PDX1, GSE14914835). Source data associated to figure panels are provided in the Supplementary Data 1.

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Источник: https://www.nature.com/articles/s42003-021-02818-3

Malware

Portmanteau for malicious software

Malware (a portmanteau for malicious software) is any software intentionally designed to cause damage to a computer, server, client, or computer network.[1][2] By contrast, software that causes unintentional harm due to some deficiency is typically described as a software bug.[3] A wide variety of malware types exist, including computer viruses, worms, Trojan horses, ransomware, spyware, adware, rogue software, wiper and scareware.

Programs are also considered malware if they secretly act against the interests of the computer user. For example, at one point, Sony BMG compact discs silently installed a rootkit on purchasers' computers with the intention of preventing illicit copying, but which also reported on users' listening habits, and unintentionally created extra security vulnerabilities.[4]

A range of antivirus software, firewalls and other strategies are used to help protect against the introduction of malware, to help detect it if it is already present, and to recover from malware-associated malicious activity and attacks.[5]

Purposes[edit]

This pie chart shows that in 2011, 70% of malware infections were by Trojan horses, 17% were from viruses, 8% from worms, with the remaining percentages divided among adware, backdoor, spyware, and other exploits.

Many early infectious programs, including the first Internet Worm, were written as experiments or pranks.[6] Today, malware is used by both black hat hackers and governments to steal personal, financial, or business information.[7][8]

Malware is sometimes used broadly against government or corporate websites to gather guarded information,[9] or to disrupt their operation in general. However, malware can be used against individuals to gain information such as personal identification numbers or details, bank or credit card numbers, and passwords.

Since the rise of widespread broadbandInternet access, malicious software has more frequently been designed for profit. Since 2003, the majority of widespread viruses and worms have been designed to take control of users' computers for illicit purposes.[10] Infected "zombie computers" can be used to send email spam, to host contraband data such as child pornography,[11] or to engage in distributed denial-of-serviceattacks as a form of extortion.[12]

Programs designed to monitor users' web browsing, display unsolicited advertisements, or redirect affiliate marketing revenues are called spyware. Spyware programs do not spread like viruses; instead they are generally installed by exploiting security holes. They can also be hidden and packaged together with unrelated user-installed software.[13] The Sony BMG rootkit was intended to prevent illicit copying; but also reported on users' listening habits, and unintentionally created extra security vulnerabilities.[4]

Ransomware affects an infected computer system in some way, and demands payment to bring it back to its normal state. There are two variations of ransomware, being crypto ransomware and locker ransomware.[14] Locker ransomware just locks down a computer system without encrypting its contents, whereas the traditional ransomware is one that locks down a system and encrypts its contents. For example, programs such as CryptoLockerencrypt files securely, and only decrypt them on payment of a substantial sum of money.[15]

Some malware is used to generate money by click fraud, making it appear that the computer user has clicked an advertising link on a site, generating a payment from the advertiser. It was estimated in 2012 that about 60 to 70% of all active malware used some kind of click fraud, and 22% of all ad-clicks were fraudulent.[16]

In addition to criminal money-making, malware can be used for sabotage, often for political motives. Stuxnet, for example, was designed to disrupt very specific industrial equipment. There have been politically motivated attacks which spread over and shut down large computer networks, including massive deletion of files and corruption of master boot records, described as "computer killing." Such attacks were made on Sony Pictures Entertainment (25 November 2014, using malware known as Shamoon or W32.Disttrack) and Saudi Aramco (August 2012).[17][18]

Infectious malware[edit]

Main articles: Computer virus and Computer worm

The best-known types of malware, viruses and worms, are known for the manner in which they spread, rather than any specific types of behavior. A computer virus is software that embeds itself in some other executable software (including the operating system itself) on the target system without the user's knowledge and consent and when it is run, the virus is spread to other executables. On the other hand, a worm is a stand-alone malware software that actively transmits itself over a network to infect other computers and can copy itself without infecting files. These definitions lead to the observation that a virus requires the user to run an infected software or operating system for the virus to spread, whereas a worm spreads itself.[19]

Concealment[edit]

These categories are not mutually exclusive, so malware may use multiple techniques.[20] This section only applies to malware designed to operate undetected, not sabotage and ransomware.

See also: Polymorphic packer

Viruses[edit]

Main article: Computer virus

A computer virus is software usually hidden within another seemingly innocuous program that can produce copies of itself and insert them into other programs or files, and that usually performs a harmful action (such as destroying data).[21] An example of this is a PE infection, a technique, usually used to spread malware, that inserts extra data or executable code into PE files.[22]

Screen-locking ransomware[edit]

Main article: Ransomware

Lock-screens, or screen lockers is a type of “cyber police” ransomware that blocks screens on Windows or Android devices with a false accusation in harvesting illegal content, trying to scare the victims into paying up a fee.[23] Jisut and SLocker impact Android devices more than other lock-screens, with Jisut making up nearly 60 percent of all Android ransomware detections.[24]

Encryption-based ransomware[edit]

Main article: Ransomware

Encryption-based ransomware, like the name suggests, is a type of ransomware that encrypts all files on an infected machine. These types of malware then display a pop-up informing the user that their files have been encrypted and that they must pay (usually in Bitcoin) to recover them. Some examples of encryption-based ransomware are CryptoLocker and WannaCry. [25]

Trojan horses[edit]

Main article: Trojan horse (computing)

A Trojan horse is a harmful program that misrepresents itself to masquerade as a regular, benign program or utility in order to persuade a victim to install it. A Trojan horse usually carries a hidden destructive function that is activated when the application is started. The term is derived from the Ancient Greek story of the Trojan horse used to invade the city of Troy by stealth.[26][27][28][29][30]

Trojan horses are generally spread by some form of social engineering, for example, where a user is duped into executing an email attachment disguised to be unsuspicious, (e.g., a routine form to be filled in), or by drive-by download. Although their payload can be anything, many modern forms act as a backdoor, contacting a controller (phoning home) which can then have unauthorized access to the affected computer, potentially installing additional software such as a keylogger to steal confidential information, cryptomining software or adware to generate revenue to the operator of the trojan.[31] While Trojan horses and backdoors are not easily detectable by themselves, computers may appear to run slower, emit more heat or fan noise due to heavy processor or network usage, as may occur when cryptomining software is installed. Cryptominers may limit resource usage and/or only run during idle times in an attempt to evade detection.

Unlike computer viruses and worms, Trojan horses generally do not attempt to inject themselves into other files or otherwise propagate themselves.[32]

In spring 2017 Mac users were hit by the new version of Proton Remote Access Trojan (RAT)[33] trained to extract password data from various sources, such as browser auto-fill data, the Mac-OS keychain, and password vaults.[34]

Rootkits[edit]

Main article: Rootkit

Once malicious software is installed on a system, it is essential that it stays concealed, to avoid detection. Software packages known as rootkits allow this concealment, by modifying the host's operating system so that the malware is hidden from the user. Rootkits can prevent a harmful process from being visible in the system's list of processes, or keep its files from being read.[35]

Some types of harmful software contain routines to evade identification and/or removal attempts, not merely to hide themselves. An early example of this behavior is recorded in the Jargon File tale of a pair of programs infesting a Xerox CP-V time sharing system:

Each ghost-job would detect the fact that the other had been killed, and would start a new copy of the recently stopped program within a few milliseconds. The only way to kill both ghosts was to kill them simultaneously (very difficult) or to deliberately crash the system.[36]

Backdoors[edit]

Main article: Backdoor (computing)

A backdoor is a method of bypassing normal authentication procedures, usually over a connection to a network such as the Internet. Once a system has been compromised, one or more backdoors may be installed in order to allow access in the future,[37] invisibly to the user.

The idea has often been suggested that computer manufacturers preinstall backdoors on their systems to provide technical support for customers, but this has never been reliably verified. It was reported in 2014 that US government agencies had been diverting computers purchased by those considered "targets" to secret workshops where software or hardware permitting remote access by the agency was installed, considered to be among the most productive operations to obtain access to networks around the world.[38] Backdoors may be installed by Trojan horses, worms, implants, or other methods.[39][40]

Evasion[edit]

Since the beginning of 2015, a sizable portion of malware has been utilizing a combination of many techniques designed to avoid detection and analysis.[41] From the more common, to the least common:

  1. evasion of analysis and detection by fingerprinting the environment when executed.[42]
  2. confusing automated tools' detection methods. This allows malware to avoid detection by technologies such as signature-based antivirus software by changing the server used by the malware.[43]
  3. timing-based evasion. This is when malware runs at certain times or following certain actions taken by the user, so it executes during certain vulnerable periods, such as during the boot process, while remaining dormant the rest of the time.
  4. obfuscating internal data so that automated tools do not detect the malware.[44]

An increasingly common technique (2015) is adware that uses stolen certificates to disable anti-malware and virus protection; technical remedies are available to deal with the adware.[45]

Nowadays, one of the most sophisticated and stealthy ways of evasion is to use information hiding techniques, namely stegomalware. A survey on stegomalware was published by Cabaj et al. in 2018.[46]

Another type of evasion technique is Fileless malware or Advanced Volatile Threats (AVTs). Fileless malware does not require a file to operate. It runs within memory and utilizes existing system tools to carry out malicious acts. Because there are no files on the system, there are no executable files for antivirus and forensic tools to analyze, making such malware nearly impossible to detect. The only way to detect fileless malware is to catch it operating in real time. Recently these types of attacks have become more frequent with a 432% increase in 2017 and makeup 35% of the attacks in 2018. Such attacks are not easy to perform but are becoming more prevalent with the help of exploit-kits. [47][48]

Vulnerability[edit]

Main article: Vulnerability (computing)

  • In this context, and throughout, what is called the "system" under attack may be anything from a single application, through a complete computer and operating system, to a large network.
  • Various factors make a system more vulnerable to malware:

Security defects in software[edit]

Malware exploits security defects (security bugs or vulnerabilities) in the design of the operating system, in applications (such as browsers, e.g. older versions of Microsoft Internet Explorer supported by Windows XP[49]), or in vulnerable versions of browser plugins such as Adobe Flash Player, Adobe Acrobat or Reader, or Java SE.[50][51] Sometimes even installing new versions of such plugins does not automatically uninstall old versions. Security advisories from plug-in providers announce security-related updates.[52] Common vulnerabilities are assigned CVE IDs and listed in the US National Vulnerability Database. Secunia PSI[53] is an example of software, free for personal use, that will check a PC for vulnerable out-of-date software, and attempt to update it.

Malware authors target bugs, or loopholes, to exploit. A common method is exploitation of a buffer overrun vulnerability, where software designed to store data in a specified region of memory does not prevent more data than the buffer can accommodate being supplied. Malware may provide data that overflows the buffer, with malicious executable code or data after the end; when this payload is accessed it does what the attacker, not the legitimate software, determines.

Anti-malware is a continuously growing threat to malware detection.[54] According to Symantec’s 2018 Internet Security Threat Report (ISTR), malware variants number has got up to 669,947,865 in 2017, which is the double of malware variants in 2016.[54]

Insecure design or user error[edit]

Early PCs had to be booted from floppy disks. When built-in hard drives became common, the operating system was normally started from them, but it was possible to boot from another boot device if available, such as a floppy disk, CD-ROM, DVD-ROM, USB flash drive or network. It was common to configure the computer to boot from one of these devices when available. Normally none would be available; the user would intentionally insert, say, a CD into the optical drive to boot the computer in some special way, for example, to install an operating system. Even without booting, computers can be configured to execute software on some media as soon as they become available, e.g. to autorun a CD or USB device when inserted.

Malware distributors would trick the user into booting or running from an infected device or medium. For example, a virus could make an infected computer add autorunnable code to any USB stick plugged into it. Anyone who then attached the stick to another computer set to autorun from USB would in turn become infected, and also pass on the infection in the same way.[55] More generally, any device that plugs into a USB port – even lights, fans, speakers, toys, or peripherals such as a digital microscope – can be used to spread malware. Devices can be infected during manufacturing or supply if quality control is inadequate.[55]

This form of infection can largely be avoided by setting up computers by default to boot from the internal hard drive, if available, and not to autorun from devices.[55] Intentional booting from another device is always possible by pressing certain keys during boot.

Older email software would automatically open HTML email containing potentially malicious JavaScript code. Users may also execute disguised malicious email attachments. The 2018 Data Breach Investigations Report by Verizon, cited by CSO Online, states that emails are the primary method of malware delivery, accounting for 92% of malware delivery around the world.[56][57]

Over-privileged users and over-privileged code[edit]

Main article: principle of least privilege

In computing, privilege refers to how much a user or program is allowed to modify a system. In poorly designed computer systems, both users and programs can be assigned more privileges than they should have, and malware can take advantage of this. The two ways that malware does this is through overprivileged users and overprivileged code.[citation needed]

Some systems allow all users to modify their internal structures, and such users today would be considered over-privileged users. This was the standard operating procedure for early microcomputer and home computer systems, where there was no distinction between an administrator or root, and a regular user of the system. In some systems, non-administrator users are over-privileged by design, in the sense that they are allowed to modify internal structures of the system. In some environments, users are over-privileged because they have been inappropriately granted administrator or equivalent status.[58]

Some systems allow code executed by a user to access all rights of that user, which is known as over-privileged code. This was also standard operating procedure for early microcomputer and home computer systems. Malware, running as over-privileged code, can use this privilege to subvert the system. Almost all currently popular operating systems, and also many scripting applications allow code too many privileges, usually in the sense that when a user executes code, the system allows that code all rights of that user. This makes users vulnerable to malware in the form of email attachments, which may or may not be disguised.[citation needed]

Use of the same operating system[edit]

Homogeneity can be a vulnerability. For example, when all computers in a network run the same operating system, upon exploiting one, one worm can exploit them all:[59] In particular, Microsoft Windows or Mac OS X have such a large share of the market that an exploited vulnerability concentrating on either operating system could subvert a large number of systems. Introducing diversity purely for the sake of robustness, such as adding Linux computers, could increase short-term costs for training and maintenance. However, as long as all the nodes are not part of the same directory service for authentication, having a few diverse nodes could deter total shutdown of the network and allow those nodes to help with recovery of the infected nodes. Such separate, functional redundancy could avoid the cost of a total shutdown, at the cost of increased complexity and reduced usability in terms of single sign-on authentication.[citation needed]

Anti-malware strategies[edit]

Main article: Antivirus software

As malware attacks become more frequent, attention has begun to shift from viruses and spyware protection, to malware protection, and programs that have been specifically developed to combat malware. (Other preventive and recovery measures, such as backup and recovery methods, are mentioned in the computer virus article). Reboot to restore software is also useful for mitigating malware by rolling back malicious alterations.

Antivirus and anti-malware software[edit]

A specific component of antivirus and anti-malware software, commonly referred to as an on-access or real-time scanner, hooks deep into the operating system's core or kernel and functions in a manner similar to how certain malware itself would attempt to operate, though with the user's informed permission for protecting the system. Any time the operating system accesses a file, the on-access scanner checks if the file is a 'legitimate' file or not. If the file is identified as malware by the scanner, the access operation will be stopped, the file will be dealt with by the scanner in a pre-defined way (how the antivirus program was configured during/post installation), and the user will be notified.[citation needed] This may have a considerable performance impact on the operating system, though the degree of impact is dependent on how well the scanner was programmed. The goal is to stop any operations the malware may attempt on the system before they occur, including activities which might exploit bugs or trigger unexpected operating system behavior.

Anti-malware programs can combat malware in two ways:

  1. They can provide real time protection against the installation of malware software on a computer. This type of malware protection works the same way as that of antivirus protection in that the anti-malware software scans all incoming network data for malware and blocks any threats it comes across.
  2. Anti-malware software programs can be used solely for detection and removal of malware software that has already been installed onto a computer. This type of anti-malware software scans the contents of the Windows registry, operating system files, and installed programs on a computer and will provide a list of any threats found, allowing the user to choose which files to delete or keep, or to compare this list to a list of known malware components, removing files that match.[60]

Real-time protection from malware works identically to real-time antivirus protection: the software scans disk files at download time, and blocks the activity of components known to represent malware. In some cases, it may also intercept attempts to install start-up items or to modify browser settings. Because many malware components are installed as a result of browser exploits or user error, using security software (some of which are anti-malware, though many are not) to "sandbox" browsers (essentially isolate the browser from the computer and hence any malware induced change) can also be effective in helping to restrict any damage done.[61]

Examples of Microsoft Windows antivirus and anti-malware software include the optional Microsoft Security Essentials[62] (for Windows XP, Vista, and Windows 7) for real-time protection, the Windows Malicious Software Removal Tool[63] (now included with Windows (Security) Updates on "Patch Tuesday", the second Tuesday of each month), and Windows Defender (an optional download in the case of Windows XP, incorporating MSE functionality in the case of Windows 8 and later).[64] Additionally, several capable antivirus software programs are available for free download from the Internet (usually restricted to non-commercial use).[65] Tests found some free programs to be competitive with commercial ones.[65][66][67] Microsoft's System File Checker can be used to check for and repair corrupted system files.

Some viruses disable System Restore and other important Windows tools such as Task Manager and Command Prompt. Many such viruses can be removed by rebooting the computer, entering Windows safe mode with networking,[68] and then using system tools or Microsoft Safety Scanner.[69]

Hardware implants can be of any type, so there can be no general way to detect them.

Website security scans[edit]

As malware also harms the compromised websites (by breaking reputation, blacklisting in search engines, etc.), some websites offer vulnerability scanning.[70] Such scans check the website, detect malware, may note outdated software, and may report known security issues.

"Air gap" isolation or "parallel network"[edit]

As a last resort, computers can be protected from malware, and infected computers can be prevented from disseminating trusted information, by imposing an "air gap" (i.e. completely disconnecting them from all other networks). However, malware can still cross the air gap in some situations. Stuxnet is an example of malware that is introduced to the target environment via a USB drive.

AirHopper,[71] BitWhisper,[72] GSMem [73] and Fansmitter[74] are four techniques introduced by researchers that can leak data from air-gapped computers using electromagnetic, thermal and acoustic emissions.

Grayware[edit]

See also: Privacy-invasive software and Potentially unwanted program

Grayware (sometimes spelled as greyware) is a term applied to unwanted applications or files that are not classified as malware, but can worsen the performance of computers and may cause security risks.[75]

It describes applications that behave in an annoying or undesirable manner, and yet are less serious or troublesome than malware. Grayware encompasses spyware, adware, fraudulent dialers, joke programs, remote access tools and other unwanted programs that may harm the performance of computers or cause inconvenience. The term came into use around 2004.[76]

Another term, potentially unwanted program (PUP) or potentially unwanted application (PUA),[77] refers to applications that would be considered unwanted despite often having been downloaded by the user, possibly after failing to read a download agreement. PUPs include spyware, adware, and fraudulent dialers. Many security products classify unauthorised key generators as grayware, although they frequently carry true malware in addition to their ostensible purpose.

Software maker Malwarebytes lists several criteria for classifying a program as a PUP.[78] Some types of adware (using stolen certificates) turn off anti-malware and virus protection; technical remedies are available.[45]

History[edit]

Main article: History of computer viruses

See also: History of ransomware

Further information: Timeline of computer viruses and worms

Before Internet access became widespread, viruses spread on personal computers by infecting executable programs or boot sectors of floppy disks. By inserting a copy of itself into the machine code instructions in these programs or boot sectors, a virus causes itself to be run whenever the program is run or the disk is booted. Early computer viruses were written for the Apple II and Macintosh, but they became more widespread with the dominance of the IBM PC and MS-DOS system. The first IBM PC virus in the "wild" was a boot sector virus dubbed (c)Brain,[79] created in 1986 by the Farooq Alvi brothers in Pakistan.[80]

The first worms, network-borne infectious programs, originated not on personal computers, but on multitasking Unix systems. The first well-known worm was the Internet Worm of 1988, which infected SunOS and VAXBSD systems. Unlike a virus, this worm did not insert itself into other programs. Instead, it exploited security holes (vulnerabilities) in network server programs and started itself running as a separate process.[81] This same behavior is used by today's worms as well.[82][83]

With the rise of the Microsoft Windows platform in the 1990s, and the flexible macros of its applications, it became possible to write infectious code in the macro language of Microsoft Word and similar programs. These macro viruses infect documents and templates rather than applications (executables), but rely on the fact that macros in a Word document are a form of executable code.[84]

Academic research[edit]

Main article: Malware research

The notion of a self-reproducing computer program can be traced back to initial theories about the operation of complex automata.[85]John von Neumann showed that in theory a program could reproduce itself. This constituted a plausibility result in computability theory. Fred Cohen experimented with computer viruses and confirmed Neumann's postulate and investigated other properties of malware such as detectability and self-obfuscation using rudimentary encryption. His 1987 doctoral dissertation was on the subject of computer viruses.[86] The combination of cryptographic technology as part of the payload of the virus, exploiting it for attack purposes was initialized and investigated from the mid 1990s, and includes initial ransomware and evasion ideas.[87]

See also[edit]

References[edit]

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External links[edit]

Look up malware in Wiktionary, the free dictionary.
Wikimedia Commons has media related to Malware.
Источник: https://en.wikipedia.org/wiki/Malware
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  1. 店を
  2. スポーツ・アウトドア
  3. アウトドア
  4. ライト・ランタン
  5. ヘッドライト
  6. ベルーフ (Beruf) ライト LEDモーションセンサーヘッドライト 150ルーメン 電池式 BHL-L06SDB 87669

ベルーフ (Beruf) ライト LEDモーションセンサーヘッドライト 150ルーメン 電池式 BHL-L06SDB 87669

4,322円

防水性能 IPX3

商品コード

他モールでも併売しているため タイミングによって在庫切れの可能性がございます その際は 別途ご連絡させていただきます

商品名

明るさ 強/約150ルーメン 弱/約80ルーメン

ベルーフ Beruf ライト LEDモーションセンサーヘッドライト 150ルーメン 電池式 BHL-L06SDB 87669

照射特性 スポット ワイド

型番

付属品 ポリシリコンバンド 1 単三形乾電池 3

光源 Cree XPE 1個

別売バッテリー 87691充電バッテリー使用可能

他モールでも併売しているため タイミングによって在庫切れの可能性がございます その際は 別途ご連絡させていただきます

点灯時間 強/約4.5時間 弱/約9時間

34043261046

87669

モーションセンサー搭載で手をかざすだけで点灯消灯できる フォーカスダイヤルで照射エリアを切替可能 点灯モード切替機能付で用途に応じて切替できる

使用環境温度 -5 40度

使用電源 単三形アルカリ乾電池 3本

ベルーフ Beruf ライト LEDモーションセンサーヘッドライト 150ルーメン 電池式 BHL-L06SDB 87669

☎ 0766-21-4700

お問い合わせ

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  • ベルーフ Beruf ライト LEDモーションセンサーヘッドライト 150ルーメン 電池式 BHL-L06SDB 87669

    ベルーフ Beruf ライト LEDモーションセンサーヘッドライト 150ルーメン 電池式 BHL-L06SDB 87669

    さらに「行こう倶楽部」登録で弊社厳選「五つ星の宿」が5%割引!!


    ベルーフ Beruf ライト LEDモーションセンサーヘッドライト 150ルーメン 電池式 BHL-L06SDB 87669

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    2021-05-25
    先日、ミシュランガイド北陸2021年発表されました!
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    これを機に、1日1組のプライベートな時間を過ごせる「売薬宿屋 山キ」でゆったりとした時間を過ごし、ミシュランで2つ星を獲得した 料理に舌鼓をうってみませんか?
    ※1日1組限定の完全貸切宿「売薬宿屋 山キ」のプランの詳細は、こちらからどうぞ
    2021-05-10
    「地元で愉しもう!とやま観光キャンペーン」申込開始!
    ご予約可能期間 5/10(月)~6/30(水)まで

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    Источник: https://softlicensekey.com/tag/pe-explorer-serial-number-2021/

    PE Explorer 1.99 R5 Download

    PE Explorer 1.99 R5 Description:

    PE Explorer is the most feature-packed tool for inspecting the inner workings of PE files (EXE, DLL, ActiveX controls, and several other Windows executable formats).

    PE Explorer offers a thorough look at PE file structure and all of the resources in the file, and tells you just about every little detail you could possibly want to know about a PE file.

    PE Explorer comes with a Visual Resource Editor, PE Header Viewer, UPX Unpacker, Exported/Imported API Function Viewer, API Function Syntax Lookup, Dependency Scanner, and powerful Disassembler.PE Explorer is a DLL viewer, resource editor, win32 PE disassembler, and dependency scanner.

    The product allows you to peek inside Delphi applications and edit the properties of controls on Delphi forms within the PE file. Once inside, file structure can be analyzed and optimized, problems diagnosed, changes made and resources repaired.

    Whether you are an advanced computer user just wondered what makes an executable file tick, or software developer suffered from hard-to-find program bugs, PE Explorer is the software solution that will make it easy to find the answers.

    With PE Explorer you can view, edit and repair the internal structures of PE files with the click of a button. It works with PE formats such as EXE, DLL, MSSTYLES, SYS, OCX, SCR and other win32 executables.

    Support for custom plug-ins. Use it for serious development projects, for restoring lost information, for keeping damaged files intact, for determination of the existence of viruses or malicious code in the programs, to reverse engineer projects with missing source code, to view the imports/exports of the standard dll's, or to simply reduce the numerous internal information sources of PE files into a more convenient viewing format and save your valuable time. The possibilities are up to you. PE Explorer gives you an easy to use point and click approach from which to operate.

    PE Explorer 1.99 R5 Features:

    · Working with PE files such as exe, dll, sys, msstyles, bpl, dpl, cpl, ocx, acm, ax, scr and other win32 executables.
    · The ability to open a broken or packed file in Safe mode.
    · Verifying a PE file's integrity.
    · Automatically unpacks files packed with UPX. Support for custom plug-ins to perform any startup processing.
    · Saving changes to disk as a new image file.
    · Customizing the general, view and logging settings. You can set the default Viewer at startup using View>Customize>General Tab.
    · Header Info Viewer displays the header information contained in the PE file header.
    · Checksum computing and modification.
    · Entry Point value modification.
    · Data Directories Viewer to view and edit Data Directories.
    · Sections Header Viewer to view, extract, recalculate or delete sections from the program body.
    · Section Editor repairs and restores the damaged section headers settings.
    · Export, Import and Delay Import Function List Viewers.
    · Quick Function Syntax Lookup displays the calling syntax for the found functions.
    · Syntax Description Editor for adding custom comments, altering values or creating new library descriptions.
    · Unmangling the exported symbols back to human-readable names.
    · Debug Info Viewer to view the debug information contained in the file.
    · Relocation Viewer to view contents of the base relocation table.
    · Resource Editor to view, delete, extract or modify nearly every type of resources.
    · XP Visual Style Manifest Wizard inserts Windows XP user interface manifests in existing applications - an easy way for legacy applications take advantage of the new look for common control styles on Windows XP.
    · Disassembler reconstructs the assembly language source code of target files.
    · Dependency Scanner helps you learn the minimum set of DLL files required for the EXE file to load and run.
    · TimeDateStamp Adjuster modifies all the TimeDate Stamps to one uniform value.
    · Remove Debug Info Tool strips the various types of debug information stored in the file.
    · Remove Relocations Tool strips the table of base relocations from the EXE files and saves space.

    PE Explorer 1.99 R5 Requirements:

    · Intel Pentium® or AMD K5 processor with 166 MHz
    · 15 MB free hard disk space
    · 16 MB RAM

    PE Explorer 1.99 R5 Limitations:

    · 30 day trial
    · Nag Screen

    Related searches:

    dll export viewer - pic disassembler - dependency visualizer

    PE Explorer security information

    You cannot download any crack or serial number for PE Explorer on this page. Every software that you are able to download on our site is legal. There is no crack, serial number, hack or activation key for PE Explorer present here. Our collection also doesn't contain any keygens, because keygen programs are being used in illegal ways which we do not support. All software that you can find here is freely downloadable and legal.

    PE Explorer installation package is prepared to be downloaded from our fast download servers. It is checked for possible viruses and is proven to be 100% clean and safe. Various leading antiviruses have been used to test PE Explorer, if it contains any viruses. No infections have been found and downloading PE Explorer is completelly problem free because of that reason. Our experts on malware detection tested PE Explorer with various spyware and malware detection programs, including fyxm.net custom malware and spyware detection, and absolutelly no malware or spyware was found in PE Explorer.

    All software that you can find on our servers, including PE Explorer, is either freeware, shareware or open-source, some of the software packages are demo, trial or patch versions and if possible (public domain licence), we also host official full versions of software.

    Because we want to be one of the fastest download sites on the web, we host all the software including PE Explorer on our servers. You cannot find here any torrents or download links that would lead you to dangerous sites.

    Fyxm.net does support free software, however we do not support warez or illegal downloads. Warez is harming producers of the software.

    Enjoy!

    Источник: http://downloads.fyxm.net/PE-Explorer-69595.html
    Mirror  (9.8 MB)

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    mazhar
    Источник: https://cracksforpc.com/pe-explorer-crack/
    PE Explorer Activation key

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