The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research. While the gut microbiome plays an important role as a modulator of host health and disease, commensal colonizers are often susceptible to disruption, which has been shown to be associated with the development of disease states4. The advancement of sequencing technologies has fueled the rapid expansion of metagenomic data availability, enabling association studies between the human microbiome and various disease states6. While many microbiome studies rely on differential analysis to identify individual bacteria of interest between cohorts, the ability of network analysis to provide high level insights into global and local structures makes it an attractive approach to study the dynamic nature of microbial communities.The gut microbiome serves to provide a wide range of symbiotic functions, including metabolism, immune system development, and pathogen resistance10. Features of metagenomic data pose several challenges to microbial co-occurrence analysis. Firstly, as sequencing technologies are not able to capture the true absolute microbiome abundance of samples, sequence abundances need to be represented as a proportion, rendering species abundance compositional by nature11. However, relative abundances, a common measure used to represent microbial abundances, is often considered a flawed metric to use in co-occurrence-based approaches due to the constant sum constraint, where assumptions of correlation metrics such as the independence between features are violated13. As relative abundances of species are dependent on the relative abundances of every other species present, abundance values of a given sample are no longer independent of each other when normalized to relative abundances. As such, alternative methods of normalization or transformation of raw abundance values remain necessary to compare species co-abundances across samples of varying sequencing depths. Additionally, the use of compositionally aware association measures and methods are needed to handle the compositionality of microbiome datasets16. Various methods have been proposed to address the challenges of analyzing compositional data, and these methods that have been reviewed in detail20. Secondly, microbiome data is often subjected to issues of sparsity, where microbiome abundance matrices are zero-inflated due to heterogeneity within and between samples. Rare taxonomic species and/or insufficient sequencing depths contribute to the sparsity often seen in microbiome datasets22. The sparsity found in metagenomic datasets introduces challenges to log-ratio based transformation techniques used to handle compositionality. Additionally, correlations of sparse datasets can lead to strong spurious correlations21. Non-parametric and ranked-based correlation measures such as Spearman’s Rho and Kendall’s Tau are also susceptible to multi-way ties due to matrix sparsity and heavy-tailed distributions, and quickly deteriorate in presence of many zeros24. Thirdly, indirect correlations can often add noise to correlation-based interaction inference methods, where these indirect associations (e.g. spurious associations) can be driven by indirect species associations, batch effect, or environmental factors27.Metagenomic co-occurrence has been widely applied in metagenomic studies to construct microbiome networks and better understand microbiome community structures28, CoNet29, CCLasso30) and conditional dependence methods  which try to differentiate between direct and indirect conditional dependencies. While the review and benchmarking of available methods’ performance remains beyond the scope of this paper, the discussion surrounding the complexities of various microbial inference techniques have been reviewed at length33. As studies have previously shown, the results of networks generated from microbial association inference are largely dependent on the method used to infer the microbial interactions33. Methods of interaction inference vary largely between studies in terms of accuracy and precision, and no one existing tool is able to address all issues of biases or confounding factors33.Despite the challenges of utilizing co-occurrence metrics on metagenomic datasets, a wide range of methods have been adopted, developed, and utilized to better understand microbial associations. In general, methods used to study microbial associations can be grouped into two categories: (1) traditional/classical correlation methods , and (2) compositionally-aware methods. While compositionally-aware methods vary in their algorithms, they all seek to mitigate the confounding factors imposed by the current limitations of compositionality found in microbiome datasets. Compositionally-aware methods can be further sub-categorized into correlation-based methods  still remains the most widely used type of association metric. The slow adaptation of compositionally aware methods for metagenomic data remains multi-factor and can likely be attributed to the exponential increase in computational requirements of compositionally aware methods, as well as legacy effect where researchers adopt the methods used in previous studies.35, where Gupta et al. used these datasets to identify 7 health-prevalent and 43 health-scarce bacterial species, from which they developed a Gut Microbiome Health Index (GMHI) for evaluating health status based on the species-level taxonomic profile of a stool microbiome sample. While the meta-analysis preformed by Gupta et al. was able to demonstrate improved patient stratification between healthy and diseased microbiomes compared to common alpha-diversity measures, remaining misclassification between samples demonstrates the complexities of defining a stratification criterion owing to our limited understanding of gut microbial ecology and their relation to human health. To build on these efforts, we constructed microbial association networks utilizing a subset of samples used by Gupta et al. Furthermore, we focus our efforts in analyzing diseases individually, in contrast to a disease-agnostic approach utilized by Gupta et al., to better characterize individual disease microbial community traits. By doing so, we expand the existing literature by uncovering microbiome community associations and community assembly dynamics within and between healthy and diseased microbial communities in an effort to identify features to help stratify disease states and potential microbial risk factors beyond individual species. Additionally, to better understand community interactions across phenotypes, we also introduce a new measure termed ‘module resilience’ to study microbial community modules retention across microbial interaction networks.Here in this study, we utilize a large collection of healthy and disease gut metagenome datasets to preform a meta-analysis using microbiome association networks by re-analyzing and standardizing the analysis approach. We note that the datasets used in this study were originally compiled in35 to be used this study. Gupta et al. used a total of 4347 samples from 78 different study accessions, with samples spanning 13 different phenotypes. In our analysis, we only included 4143 samples from 10 phenotypes: Healthy, advanced  adenomas (AA), atherosclerotic cardiovascular disease (ACVD), colorectal cancer (CRC), Crohn’s disease (CD), obesity (OB), overweight (OW), rheumatoid arthritis (RA), Type-2 diabetes (T2D), ulcerative colitis (UC). Samples from the following phenotypes included in Gupta et al., impaired glucose tolerance (IGT), symptomatic atherosclerosis (SA), and underweight (UW) were excluded from downstream analysis due to low sample count. Samples were downloaded from NCBI Sequence Read Archive via SRA Toolkit’s fasterq-dump.A curated list of sample accession numbers from publicly available human gut metagenome datasets was gathered from Gupta et al.36, where marker gene based methods ranked among the lowest among assessed tools in terms of precision and recall for species classification and lowest proportion of abundance quantified at species-rank. Secondly, while maintaining similar study accessions, we insured that all run accessions downloaded focused on available paired-end reads with the largest available spots rather than utilizing a mix of single-ended and paired-ended reads. Finally, our meta-analysis focuses on species co-occurrences and network-based approaches rather than focusing on the prevalence of species-level abundances between samples, and the bacterial networks resulted from our analyses can be used by other researchers for different research purposes.A summary of samples used in this study can be found in Table 37 with parameters SLIDINGWINDOW:4:20 LEADING:20 TRAILING:20 MINLEN:60. Trimmed samples were then mapped to the human genome assembly GRCh38 (hg38) using bowtie2 (v2.4.4)38 to remove possible human read contamination from the metagenome samples. All remaining unmapped metagenomic reads were kept for downstream analysis. Additionally, low read count samples that were less than 1M reads were discarded from this analysis to prevent inclusion of under-sampled genomes. Distribution of the filtered reads can be found on Supplementary Fig. Samples were processed to remove low quality reads and Illumina adapters using Trimmomatic (v0.39)39. The pre-built ‘Standard’ Kraken2 database (version k2_standard_20201202) maintained by the authors of Kraken2, built on December 2, 2020, was used as taxonomic references (https://genome-idx.s3.amazonaws.com/kraken/k2_standard_20201202.tar.gz). The ‘Standard’ Kraken2 database was built using RefSeq reference genomes, including references from archaea, bacteria, viral, plasmid, human, and UniVec_Core databases. Only archaea and bacterial counts were retained for downstream analysis. Kraken2 prokaryotic taxonomic assignments and abundances were then re-estimated with Bracken (v2.6.2)40 for species-level re-estimation of abundances. Samples were aggregated into their representative disease phenotype to construct species level read abundance matrices.Taxonomic assignment and species abundance quantification were preformed using Kraken2 (v2.0.8)14. To address some of the issues caused by matrix sparsity, we employed a method of filtering based on species prevalence similar to those suggested by41. To determine the level of species prevalence to filter, we empirically evaluated the species:species-prevalence distribution within our datasets to determine a species prevalence threshold that minimized zero-inflation while retaining majority of species locally observed within each respective phenotypic group  and can cause spurious correlations and false-positives in statistical methods25 was selected as the association method for microbial association inference due to the method accounting for compositionality of microbiome data and potential indirect associations. SPIEC-EASI was run using the ‘MB’ method, a neighborhood selection method developed by Meinshausen and Bühlmann42 used to infer sparse inverse covariance matrices from a network. SPIEC-EASI has been found to preform well in comparison with other association methods, and thus selected to be used in this analysis26.Using prevalence filtered Bracken reads count abundance matrices, species-level associations were inferred for each disease abundance matrices respectively. SPIEC-EASI26. Networks were visualized through Gephi43 using Force Atlas 2 layout. All singleton nodes without edges were removed from the network.Microbiome co-occurrence network were constructed from association values computed using SPIEC-EASI, where values were filtered with a 0.1 absolute association value threshold. Network vertices were defined as prokaryotic species for species-level networks; vertices and node are used synonymously throughout. An undirected edge was constructed between two vertices if a significant association between two given vertices was inferred. Edge weights range between , where positive edges represent a positive association and negative edges represent negative associations. It should be noted that edge weights of conditional dependence methods cannot be directly compared to correlation based metrics and are not directly proportional even though their values are assessed on the same scale In addition, a consensus network was constructed to analyze Proteobacteria interactions among the disease networks. Given an edge, if any vertices within that edge had an annotated Genus as Proteobacteria, the edge was kept. Utilizing all remaining edges, a consensus network for each disease was built, where the edge weight was equivalent to the number of networks containing each respective edge.44, which attempts to extend on the works of the Louvain algorithm45. The Louvain algorithm can sometimes have badly connected communities, whereas the Leiden algorithm guarantees that communities are well connected and locally optimized. The Leiden algorithm consists of three steps, first it performs a local moving of nodes, second it refines partitions, and lastly the aggregation of the network based on the refined partitions. The Leiden algorithm takes advantage of local moving procedure and is able to split clusters rather than only merging them as in the Leiden algorithm. Additionally, the Leiden algorithm is able to handle negative edge weights.Many methods developed for community module detection in network systems are based off of undirected, unsigned, and positive networks. However, methods for signed module detection remain largely under-explored. In many cases, negative edges are simply discarded or ignored. However, as microbiome interactions are highly-dynamic and involve not only positive interactions, it is important to maintain the use of signed interactions when possible. To address this challenge, we utilized the Leiden algorithmi found in healthy network containing j that contains the most members of the i staying in the same community in diseased network j). The resilience of module i is defined as the median of K is the number of diseased networks , then i has a resilience score of 0.75 (the median). See Fig. We proposed a resilience score to approximate the tendency of modules of gut bacterial species detected from the healthy microbiome network to remain in the same community in the gut microbiome associated with different diseases. Given a module entclass1pt{minimahttps://github.com/mgtools/GutNet. Sample run accession numbers and associated study accession for all publicly available stool metagenome samples used in this study are available in the repository.All network (GML) files, bioinformatics workflows, and analysis scripts produced as part of this study can be found in a GitHub repository 48.The total number of species annotated in all datasets was 6463, spanning 4143 samples , a prevalence filter of 50% as suggested byU test, p-value U test, p-value U test, p-value U test, p-value < 0.05). Observations of significant differences in alpha diversity measures between healthy and diseased datasets are in line with previous studies that have used alpha-diversity measures as an indicator of disease-associated microbiome dysbiosis50.To evaluate the alpha-diversity between healthy and diseased microbiome datasets, we utilized the Shannon diversity index and species richness (observed number of different species) measures per each phenotype Fig. . For the35) also showed no clear clusters of the samples according to phenotypes, but in their study, an ANOSIM test showed weak difference between among- and within-group dissimilarities.For beta-diversity analysis, we used ordination plots to summarize the microbiome community data of healthy population and individuals with diseases. We used Bray-Curtis dissimilarity as the distance measure between the datasets, and used both t-SNE and NMDS approaches for dimensionality reduction. In the 2-dimensional ordination space shown in Supplementary Fig. To better understand microbiome associations and microbial community interactions in healthy and diseased gut microbiomes, we identified microbiome community modules within each microbiome network  used to compute the GMHI score. This contrast suggests that beyond differential microbial abundances, microbial interactions can also play a pivotal role in stratifying microbiome disease states.By examining the species  and their interactions  in the microbial association network, we can study their contribution to microbial community assembly. Analyzing the nodes of constructed association networks, we found that the top Phyla in each association networks comprised of Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, Euryarchaeota, and Cyanobacteria , we focused majority of our analysis by agglomerating at the Phylum level, but acknowledge there remains much more to be explored at lower taxonomic levels. There remains an increasing need for gold standards to be developed so that tools and methods can be benchmarked and evaluated to establish standardized protocols. Future efforts in development of experimental and computational methods are necessary to address issues of microbiome compositionality.56. While Force Atlas 2 may have certain limitations that need to be mindful of when interpreting visual representations of generated networks, all results here reported were derived from computational network models, and thus we believe that the findings of this work will not be impacted by network layout limitations if they are present.For network visualization, we utilized Force Atlas 2 which we note that the network layout depends on initial state of coordinates and can become stuck at local minimums12 and Cao et al.41 have suggested such prevalence filtering as an effective means to mitigate these challenges, both initially were based on 16S sequencing. However, their recommendations were made to address issues of sparsity and its influence on analysis of microbiome datasets, and thus their recommendations extended beyond 16S sequencing to include sparse abundance matrices also commonly found in metagenomic datasets. In fact, the practice of filtering for species prevalence is also commonly used to filter metagenomic sequencing results as a means to account for the same statistical issues, and remains particularly critical in correlation based analyses. Examples include those of Milanese et al.57 that suggests the use of prevalence filtering from metagenomics abundance matrices to mitigate potential spurious correlations between low-prevalence, and Llyod-Prince et al.58 that utilizes prevalence filtering to reduce affects of zero-inflation in metagenomic abundance matrices. Filtering methods will inevitably filter out species that are true-positives and there remains a possibility that some of these filtered species may play an integral part in influencing a given microbiome state. However, by utilizing a prevalence filtering method rather than an abundance filter, species that are observed homogeneously in within-phenotype microbiomes are retained, including low abundance species. Without filtering for species prevalence, correlation based analyses risk the inclusion of spurious correlations that do not reflect true correlation but rather statistical artifacts.We utilized the method of filtering for species prevalence as a means to mitigate potential statistical challenges resulting from sparse metagenomic abundances. While the recommendations from Weiss et al.35, three health-prevalent Bifidobacterium species  and one health-scarce Bifidobacterium species (B. dentium) were found in all 10 healthy and disease association networks we derived, and it would be interesting to examine the interactions between these Bifidobacterium and other species in the networks and the differences across networks. While it is not possible to assess and benchmark the wide availability of microbial association methods, standardizing the protocols and processing steps of data analysis will help future efforts to uncover features that warrant further investigation. Here we provide all microbial association networks produced as part of this study as a resource for future efforts in studying microbial associations. By preforming meta-analyses, results of individual studies can reach beyond itself and assist in contextualizing new results through expanding insights in comparison to other studies. Nevertheless, computational microbiome association methods remain insufficient by themselves to identify causal interactions. Association analysis can only serve as a starting point to reduce the search space and identify potential candidates for downstream hypothesis testing and experimental validation.Despite these limitations, our results uncovered features of microbiome association between healthy and diseased cohorts that may help future efforts in understanding alterations of the gut microbiome that may be associated with diseased states. For example, among the health-prevalent and health-scarce species identified by Gupta et al.We proposed a pipeline for microbiome association network inference that incorporates the recent advances in network inference approaches that can deal with sparse compositional data and tease apart indirect vs direct interactions. Through meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states. By focusing our analysis on microbial networks, we show that microbial interactions can extend approaches to stratify between microbiome associated disease phenotypes beyond differential abundances. The findings of this study add to the body literature to inform future efforts in microbiome related disease stratification efforts as well as efforts to better understand microbial community interactions. We made available the inferred healthy and diseased microbiome association networks in a standard network format and we anticipate that they will become an important resource for human-related microbiome research.Supplementary Figures.


The potential roles of breast cancer stem cells (BCSCs) in tumor initiation and recurrence have been recognized for many decades. Due to their strong capacity for self-renewal and differentiation, BCSCs are the major reasons for poor clinical outcomes and low therapeutic response. Several hypotheses on the origin of cancer stem cells have been proposed, including critical gene mutations in stem cells, dedifferentiation of somatic cells, and cell plasticity remodeling by epithelial-mesenchymal transition (EMT) and the tumor microenvironment. Moreover, the tumor microenvironment, including cellular components and cytokines, modulates the self-renewal and therapeutic resistance of BCSCs. Small molecules, antibodies, and chimeric antigen receptor (CAR)-T cells targeting BCSCs have been developed, and their applications in combination with conventional therapies are undergoing clinical trials. In this review, we focus on the features of BCSCs, emphasize the major factors and tumor environment that regulate the stemness of BCSCs, and discuss potential BCSC-targeting therapies. The mammary epithelium is composed of two main cellular lineages: luminal epithelial cells and myoepithelial cells. Accumulated evidence from both mice and humans showed that these two epithelial cells are derived from common ancestors, namely the mammary epithelial stem cells +PR+ luminal cells -PR- luminal progenitors share similar expression patterns with basal-like breast cancers Interestingly, a correlation between breast cancer subtypes and distinct stem cell populations is observed. Based on the expressions of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor 2 (HER2/ERBB2), breast cancer is simply classified into three major subtypes: luminal type with hormone receptor positive/ERBB2 negative (70% of patients), ERBB2 positive (15-20%), and triple-negative  Surgery is usually the first choice for breast cancer treatment. Most women may receive additional treatment, including chemotherapy, hormone therapy, radiation or targeted therapy. More than 90% breast cancer don't occur metastasis at the time of diagnosis Conventional therapies for breast cancer are insufficient to eliminate all cancer cells in the tumors, especially cancer stem cells (CSCs), which can lead to recurrence and drug resistance In this review, we comprehensively introduced the origin of BCSCs and summarized the latest research progresses on BCSCs, including features, functions, and targeting strategies. Finally, we discuss future research directions in this field.The observation that BCSCs share many traits with MaSCs indicated that these two cell populations might have a common origin. For example, CD44 and PROCR label both MaSCs and BCSCs + cancer cells were reported to be featured by strong self-renewal potential and contributed to vasculogenic mimicry (VM) in TNBC + BCSCs are distinct from CD24-CD44+ BCSCs, suggesting BCSC populations are heterogenous CD133 is initially discovered in hematopoietic stem cells and bone marrow-derived circulating endothelial progenitors Integrin α6, also known as CD49f, may be the only biomarker present in more than 30 different populations of stem cells including embryonic stem cells, embryonic neuronal stem cells, hematopoietic stem cells, and cancer stem cells +/CD44- MCF-10A cells and MCF-7 breast cancer cells and found a small population of CD24-/CD44+ stem-like cells emerged Another speculation on the origin of cancer stem cells is EMT, an essential program for embryonic development. The first work on the relationship between EMT and stemness was conducted in the mammary gland. microRNA (miRNA)-200 family comprises five members  and all members were markedly downregulated in cells with EMT -/CD44+/ALDH+ BCSC population with intermediate EMT characteristics had the most vital capacity for stemness and tumorigenesis +/CD44hi breast cancer cells expressing both epithelial and mesenchymal markers. Complete transition to a mesenchymal state decreases their capacity for stemness and tumorigenesis Acquisition of a partial EMT phenotype or a hybrid E/M state seems more critical for stemness and tumorigenicity of breast cancer cells. Liu's study found the first evidence of an association between BCSC heterogeneity and EMT. They discovered that a CD24Another model currently predominates in explaining the origin of CSC is the stochastic model because CSC and non-CSC interconvert. Thus, CSC cannot be isolated by cell-sorting methods based on intrinsic features -CD10-CD24-PROCR+CD44+ were identified in normal mammary epithelium and breast carcinomas. Notably, both PROCR and CD44 are target genes of the Wnt pathway +CD24- BCSCs  in vivoGenetic and epigenetic alterations and abnormal activation of signaling pathways can promote the malignant transformation of normal stem cells. Human stem cells with the surface marker phenotype LinIn addition, several studies have reported that some mammary epithelial cells in a dormant state might give rise to BCSCs under specific circumstances. For example, Guo et al. found that co-expression of SRY-box transcription factor 9 (SOX9) and SLUG in differentiated luminal cells was sufficient to induce stem cell-like properties, such as EMT activation and metastasis-seeding ability CSCs in tumors exhibit very heterogeneous metabolic states, and each CSC has an adaptable metabolism. Somatic stem cells Figure 1).BCSCs are engaged in the physical behaviors of cancer, including recurrence, metastasis, vasculogenic mimicry, angiogenesis, and therapeutic resistance . They also observed intratumor vessels of human origin in transplanted tumors + BCSCs were observed to line VM channels, and breast cancer cells encircled VM channels. They also observed a close correlation between BCSC proportion and VM in invasive breast cancer Recent studies indicated that BCSCs might contribute to tumor-associated angiogenesis by VM or trans-differentiation CSCs are closely related to tumor metastasis Inactivation of tumor suppressor genes and activation of oncogenes also regulate the metastatic potential of breast cancer stem cells. Knockout of p21 in a breast tumor model inhibited the self-renewal and lung metastasis of BCSCs Overexpression of miR-7 in BCSCs decreases endothelial cell adhesion molecule (ESAM) expression by targeting RelA and inhibits tumorigenesis and distant lung metastasis LncRNA MALAT-1 is upregulated in BCSCs, and it promotes self-renewal, migration, and invasion of BCSCs by regulating SOX2 Current studies have shown that BCSCs are one of the main reasons for radiation resistance and chemotherapy resistance. Yang et al. reported that aurora kinase A (AURKA) promotes forkhead box M1 (FOXM1) transcription, self-renewal, and drug resistance in BCSCs The drug resistance of BCSCs is mainly caused by the expression of transporters that cause drug efflux and the expression of high-level ALDH1 as a detoxification enzyme to metabolize anticancer substances. In addition, BCSCs have a strong DNA damage repair ability and show radiation resistance through high expression of related stemness genes and activation of antiapoptotic and antioxidant signaling pathways ALDH is closely related to BCSC therapeutic resistance. One of the reasons is that it can inactivate the metabolism of chemotherapy drugs such as cyclophosphamide In addition, Hippo signaling is emphasized to play a key role in mediating therapeutic resistance. For example, the hyperactivation of YAP and TAZ is indicative of resistance to paclitaxel, lapatinib, doxorubicin, or CDK4/6 inhibitors The resistance of BCSCs to radiotherapy may be caused by apoptosis reduction and DNA damage checkpoint activation. Double-strand breaks in DNA damage are mainly repaired by homologous recombination (HR) or nonhomologous end-joining (NHEJ) Figure 2).The TME consists of noncellular components, such as extracellular matrix (ECM) nutrients, metabolites and cytokines, and cellular components, including fibroblasts, adipocytes, immune cells and endothelial cells. All these components exhibit dynamic changes during cancer progression and are associated with cancer stemness , a phenomenon called desmoplasia Proinflammatory cytokines produced by CAFs, such as interleukin (IL) 1β and leukemia inhibitory factor (LIF), promote the inflammatory reaction in TME +GPR77+ CAFs have been identified as a protumorigenic subpopulation that maintains the stem niche of CSCs Activation of the IL6/STAT3/ NF-κB signaling pathway in HER2-positive PTEN-depleted breast cancer cells induces an increase in the CSC population. Inhibition of IL6- or IL8-secreting CAFs slows the growth of BCSCs As the primary component in breast tissue, an increasing amount of evidence has shown that adipocytes enhance tumor malignancy by releasing inflammatory factors, metabolites, and exosomes. Indeed, the Body Mass Index (BMI) of women with breast cancer is higher than that of the general population at the time of diagnosis, and patients might benefit from modest weight loss after diagnosis  in vitro and in a PDX model Multiple hormones, such as leptin, resistin and adiponectin, which are abnormally secreted by CAAs, are also responsible for the enhanced stemness of breast cancer. Bowers et al. reduced the expression of the leptin receptor (LEPR) in breast cancer cell lines and observed dramatically decreased expression of stem cell markers The mechanisms by which obesity-induced dysfunctional adipocytes contribute to breast cancer stemness have not been clearly elucidated. Gao et al. reported that the transcription factor TAZ in adipocytes played an important role in upregulating cytokine secretion. TAZ knockdown or deficiency also impaired the tumor-supporting function of CAAs CSCs prefer to be located near the vasculature due to the conveniences of migration and nutrition acquisition. The recent discovery that BCSCs tend to gather with the arteriolar niche in ER-positive breast cancer demonstrated a bidirectional interaction driven by lysophosphatidic acid (LPA)/protein kinase D (PKD-1) signaling between BCSCs and endothelial cells Meanwhile, the endothelial cells of the vasculature secrete soluble factors to support the growth and self-renewal of CSCs and promote drug resistance. Soluble factors such as VEGFs tend to bind with tyrosine kinase receptors and thus induce tumor cell proliferation or migration + HCC1937 cells with inducible p53 transfection showed a high capacity to form tubular structures when cultured in Matrigel, while ALDH1A3- cells failed to create such structures. ALDH1A3+ cells also exhibit characteristics of CSCs and are resistant to p53-induced apoptosis + breast cancer cells represented a kind of special BCSC to initiate VM + endothelial cells and TNBC cells. They found that TEM8+ endothelial cells were killed and neovascularization was blocked and CD24-/CD44+ BCSC numbers were reduced, offering preclinical proof for immunotherapeutic targeting of tumor vascularization In addition, cancer cells might mimic the embryonic vasculogenesis process during metastasis. The vessel-like structures are favorable for periodic acid-Schiff (PAS) staining and negative for CD31. Notably, the VM showed perfusion capacity, and erythrocytes were found inside the VM, suggesting that they might support tumor cells in hypoxia by transporting nutrients and oxygen. In breast cancer, ALDH1 expression was associated with the formation of VM, especially in TNBC Endothelial cells also support BCSCs independent of their vascular functions. Pegah et al. found that the NOTCH ligand Jagged1 was secreted by endothelial cells and activated the NOTCH pathway in BCSCs; thus, endothelial cells conferred a survival advantage and metastatic potential for BCSCs. Esak et al. cocultured MDA-MB-231 cells with lymphatic endothelial cells (LECs), microvascular endothelial cells (MECs), or human umbilical vein endothelial cells (HUVECs) and found that only LECs supported tumor cell growth. They discovered that tumor-educated LECs secreted amounts of epidermal growth factor (EGF) and PDGF-BB to promote tumor growth The interaction between the host immune system and cancer progression consists of three stages: clearance, balance, and escape. During the balance phase, CSCs might be the first batch of tumor cells that escape immune surveillance. Benefiting from a long quiescent stage, CSCs are slippery and rarely targeted by the immune system even when they enter the bloodstream and are surrounded by immune cells. In addition, BCSCs express low levels of MHC class I molecules and defects in antigen processing to escape immune cell killing A large number of studies has reported the association between TAMs and poor clinical outcome. CSCs in breast cancer escape innate immune surveillance by upregulating the expression of CD47 MDSCs are a heterogeneous population of immunosuppressive cells. The high number of MDSCs in circulation is related to tumor progression, including metastasis. By depleting required amino acids, MDSCs restrict T-cell proliferation and suppress T-cell function + T cells upregulated the expression of stemness genes and immune checkpoint genes + Tregs increased the proportions of side populations and ALDH+ BCSCs in three mouse breast tumor cell lines Although the infiltration of T cells is a favorable index of chemotherapy response and patient survival, several studies revealed that Tregs promote BCSCs. Roland et al. observed that CD8Table 1). Baicalein  is an active ingredient of the roots of Scutellaria baicalensis Georgi. Baicalein reverses the drug resistance of MDA-MB-231/IR cells by upregulating interferon induced protein with tetratricopeptide repeats 2 (IFIT2) to induce the apoptosis of BCSCs Psoralea corylifolia, targets NOTCH-1 in BCSCs and inhibits EMT and breast tumor growth Nature is a treasure trove of natural compounds produced quantitatively through synthetic biology. It is a candidate drug for treating BCSCs . Palbociclib is an oral biologically effective second-generation CDK4/6 inhibitor approved by the FDA that induces cell cycle arrest in breast cancer. Palbociclib inhibits MCF-7 BCSCs and effectively slows the formation of 3D spheroids Small molecule compounds have significant advantages such as the possibility of oral administration, stability, membrane permeability and non-immunogenicity in spite of off-target toxicity. Its convenient production has higher expected benefits  in vitro and in vivo and killing BCSCs cells With the increasing difficulty of drug development, new uses of conventional drugs have broad application prospects. It is highly safe and may reduce overall development costs and shorten development time, but its use in cancer is still affected by multi-drug resistance mechanisms Nucleic acid medicines have achieved a short reaction time, long-lasting therapeutic effect, minor side effects, and high specificity. Still, their shortcomings are lacking available genetic information and limited transmission to target organs and cells. Once these urgent problems are solved, chemical synthesis can significantly improve its application ability Table . So far, there are a variety of drugs have been introduced into the clinical to bring great cure hope to patients. However, the problem is that breast cancer will develop resistance to certain drugs, and one of the reasons is the presence of CSCs. There are also a variety of therapies targeting CSCs. CAR-T therapy in immunotherapy has attracted much attention due to its high efficiency in killing cancer cells, but its fatal disadvantage is that it cannot distinguish the expression level of the same target protein in different cells, which will lead to strong side effects. At present, there are several difficulties in the treatment of CSCs. Firstly, it is difficult to distinguish the molecular characteristics of normal stem cells and CSCs. They share cell surface markers and signaling pathways, and the specificity of CSCs remains to be explored. Secondly, it is necessary to use a variety of combined treatments to extinguish the dormant and proliferating CSCs. Finally, if the CSCs can be eliminated early in the tumor, then the treatment effect will be more effective.Currently, there are several strategies to target BCSCs, each with its own advantages and disadvantages in vivo studies on BCSCs are performed in an immune-deficient environment, making it challenging to recapitulate biological complications in the clinic.Current breast cancer therapies have significantly improved in eliminating primary tumors and prolonging survival. However, effectively eliminating BCSCs is difficult. First, the dynamic intrinsic property of BCSCs and the unique CSC niche make it challenging to eradicate BCSCs. Second, BCSCs can switch their state between dormancy and rapid reproduction. Killing cells in a quiescent state is still a challenge. Third, the unique microenvironment of BCSCs is not well elucidated, and most current Although the single-cell sequencing studies did not detect a single distinct stem cell population with unique transcriptional features in the adult murine gland, these sequencing data provided a detailed transcriptional map of mammary epithelial differentiation, suggesting that scRNA-sequencing coupled with flow cytometry might be a powerful tool for CSC discovery and isolation. In addition, targeting unique regulatory factors that contribute to maintaining CSC niches in combination with traditional treatment against rapidly proliferating cancer cells will be a promising approach and has shown synergistic effects. However, the safety and efficacy of the combined strategies need to be evaluated in preclinical and clinical studies. Modulating the tumor microenvironment to target BCSCs also exhibited encouraging results. At present, there are various therapies for BCSCs, each with its own advantages and disadvantages. In addition to the drugs mentioned above, EpCAM-CAR-T (NCT02915445) has entered the clinical state for BCSCs immunotherapy, and good results are expected. Currently, only cervical cancer vaccine has entered clinical application, specific antigens for BCSCs to develop antibodies against breast cancer need further investigation. With increasing progress in BCSC research, patients will benefit from BCSC-based personal cancer treatment.This work was supported by National Key R&D Program of China (2020YFA0112300 and 2020YFA0803200), National Natural Science Foundation of China (U2102203 and 81830087), and Yunnan Fundamental Research Projects (202101AS070050). “Ten Thousand Plan” - National High-Level Talents Special Support Plan (WR-YK5202101); Program for Outstanding Leading Talents in Shanghai; Program of Shanghai Academic/Technology Research Leader (20XD1400700); Program for Outstanding Medical Academic Leader in Shanghai (2019LJ04); The innovative research team of high-level local university in Shanghai.


While providing a wealth of important data, these cell lines do not completely mimic the morphology and function of normal human intestinal epithelial cells (IEC) due to cancer-related chromosomal abnormalities and oncogenic mutations. The development of human intestinal organoids provided a physiologically-relevant experimental platform to study homeostatic regulation and disease-dependent dysfunctions of the intestinal epithelial barrier. There is need to align and integrate the emerging data obtained with intestinal organoids and classical studies that utilized colon cancer cell lines. This review discusses the utilization of human intestinal organoids to dissect the roles and mechanisms of gut barrier disruption during mucosal inflammation. We summarize available data generated with two major types of organoids derived from either intestinal crypts or induced pluripotent stem cells and compare them to the results of earlier studies with conventional cell lines. We identify research areas where the complementary use of colon cancer-derived cell lines and organoids advance our understanding of epithelial barrier dysfunctions in the inflamed gut and identify unique questions that could be addressed only by using the intestinal organoid platforms.Disruption of the intestinal epithelial barrier is a hallmark of mucosal inflammation. It increases exposure of the immune system to luminal microbes, triggering a perpetuating inflammatory response. For several decades, the inflammatory stimuli-induced breakdown of the human gut barrier was studied  Bothn family , 6. A prn family –9.Proper plasma membrane assembly of different TJ and AJ components is necessary, but not sufficient, to create and maintain the functional intestinal epithelial barrier. An additional mechanism critical for barrier regulation involves the coupling of epithelial junctions to the underlying cortical actin cytoskeleton. A circumferential actin filament belt enriched with an actin motor, non-muscle myosin II (NM II), is a prominent structural feature of well-differentiated IEC –15. ThisDisruption of the intestinal epithelial barrier is a well-recognized hallmark of mucosal inflammation. This phenomenon is best studied in chronic autoimmune disorders, such as inflammatory bowel diseases that include Crohn’s disease (CD) and ulcerative colitis (UC), as well as celiac disease –20. Addiin vitro models to recapitulate and dissect the signaling and cellular responses characteristic of the inflamed human intestine in vivo. Until recently, the experimental toolbox for in vitro studies of the human intestinal epithelial barrier included just a handful of well-differentiated colon cancer-derived cell lines , but will exclude studies of intestinal epithelial interactions with bacteria and viruses. Such a rapidly expanding field is largely focused on examining the inflammatory responses and has been recently summarized in several excellent reviews . The PSC could be established from either pluripotent embryonic stem cells or by inducing differentiated cells, such as fibroblasts, to convert into the pluripotent stage , 93. Humrganoids , 94. Imprganoids , 94. UnlDifferentiated intestinal organoids provide a unique opportunity to study the assembly and regulation of the gut epithelial barrier either in heterogeneous populations of primary epithelial cells or under more complex conditions involving epithelial-stromal interactions. Different experimental approaches have been developed to measure barrier permeability at two distinct morphological states of the organoids (3-D spheroids versus 2-D monolayers) . PermeabIntestinal crypt-derived organoids. Since the introduction of primary human intestinal organoids, they have been increasingly used for understanding the effects of inflammatory cytokines on barrier properties as well as the structure of epithelial junctions either in 3-D spheroids or 2-D organoid-derived monolayers. Most studies focused on the effects of TNFα and IFNγ because these cytokines are essential for IBD pathogenesis and have been extensively investigated using conventional human IEC lines  represent more complex structures, retaining both epithelial and mesenchymal compartments, they should more closely recapitulate epithelial responses characteristic of the complex multicellular environment of the intestinal tissue   . This isntestine . Regardlntestine . Such bantestine . Interesntestine . By contntestine .A unique response of iPSC-derived intestinal organoids to inflammatory stimuli includes the induction of fibrogenic molecular pathways. For example, organoid treatment with TNFα and IL-1α stimulated expression of extracellular matrix proteins, fibronectin and collagen, along with upregulation of a mesenchymal marker α-smooth muscle actin (α-SMA) . FurtherOverall, the described studies provided solid evidence that the integrity of epithelial barriers developed by either intestinal crypt-derived or iPSC-derived human intestinal organoids become compromised during exposure to key inflammatory cytokines characteristic for IBD mucosa. This highlights intestinal organoids as an attractive model system to evaluate the impact of inflammatory mediators on the intestinal epithelial barrier, investigate their mechanisms of action, and develop novel pharmacological approaches to prevent or reverse compromised epithelial barrier integrity in the inflamed intestinal mucosa.Comparison of intestinal organoids with conventional IEC lines. A large number of studies focusing on the disruption of the gut barrier during mucosal inflammation have been previously performed using conventional IEC lines  documented the increased barrier permeability , 133 andIFNγ alone or in the combination with TNFα was previously identified as a potent disruptor of epithelial barrier integrity in T84 and Caco-2 cell lines , 135. ThIn addition to the described actions by TNFα, and IFNγ, other inflammatory mediators appear to have consistent effects on the barrier properties of human intestinal organoids and conventional IEC lines Table 1.via the apoptotic and necroptotic mechanisms (One of the most obvious benefits of using intestinal organoids is an opportunity to study the roles and mechanisms of the programmed cell death in the inflamed mucosa. Cell death is an important homeostatic process that mediates self-renewal of the normal intestinal epithelium, where terminally-differentiated cells undergo constant apoptosis and shedding from the gut surface into the lumen , 138. Thchanisms –148. Furchanisms . This imSeveral recent studies with human intestinal organoids also suggest that IEC death is a common response to different inflammatory cytokines Table 2.via alternative pathways (It should be noted that while the described studies demonstrated the induction of cell death in cytokine-exposed human intestinal organoids, which in some instances was accompanied by increased barrier permeability and the loss of epithelial junctions , 121, a pathways , 155. PApathways , 157. Supathways Table 2 pathways . We concSeveral recent studies demonstrated that organoids generated from either intestinal crypts or iPSC of IBD and celiac disease patients preserve unique transcriptomic signatures and inflammatory features described for the intestinal epithelium in these diseases , 158–162The reported data remain fragmented and contradictory. For example, a study utilizing colonoid-derived epithelial monolayers obtained from actively inflamed tissue biopsies of UC and CD patients reported significant increases in their ionic permeability when compared to the monolayers obtained from non-IBD controls . Such baNot only barrier leakiness but also altered molecular composition of epithelial junctions observed in the intestinal mucosa of IBD patients was shown to be preserved in cultured intestinal organoids. For example, a recent study performed a side-by side comparison of the expressional profiles of different junctional proteins in surgically resected ileal samples of CD patients and non-IBD controls to enteroids generated from these tissue samples . ImportaIt remains unknown if the altered structure and permeability of epithelial junctions found in IBD intestinal mucosa could be recapitulated in human organoids generated from iPSCs of these patients. Only one study demonstrated concordant decreased expression of E-cadherin along with increased level of RhoA, an important junctional regulator, both in colonic mucosa of UC and paired iPSC-derived intestinal organoids . AnotherDefects in intestinal epithelial barrier integrity also appear to be preserved in enteroids generated from celiac disease patients. Indeed, a recent study that examined duodenal enteroid-derived IEC monolayers reported leakier barrier in celiac disease monolayers as compared to non-celiac controls . Such baHuman intestinal organoids provide a robust, physiologically-relevant experimental platform to study gastrointestinal homeostasis and diseases. A cadre of recent studies used this novel methodology to model the homeostatic development of the intestinal epithelium and characterize epithelial abnormalities in intestinal disorders , 170–172Analyzed data and participated in writing, SL, MBN, NN, FR, and AI. Supervision, AI. All authors contributed to the article and approved the submitted version.


The ongoing expansion of the cosmetic armamentarium of facial rejuvenation fails to uncover the inherent risks of cosmetic interventions. Informed consent to all risks of cosmetic filler injections and potential sequelae, including ocular and neurological complications, should be carefully ensured. We present two cases of complications following facial hyaluronic acid filler injections.Case 1: A 43-year-old woman presented with monocular vision loss of the left eye, associated ptosis, ophthalmoplegia, periocular pain and nausea, cutaneous changes of the glabella region and forehead, and sensory impairment in the left maxillary branch dermatome (V2) after receiving a hyaluronic acid (HA) filler injection into the left glabellar area. On ophthalmological examination, an ophthalmic artery occlusion (OAO) was diagnosed upon identification of a “cherry-red spot”. Magnetic resonance imaging (MRI) revealed a left ischemic optic neuropathy. Supportive therapy and hyaluronidase injections were initiated. A follow-up MRI of the head performed two months after presentation corresponded to stable MRI findings. The patient had irreversible and complete vision loss of the left eye, however, the ptosis resolved.Case 2: A 29-year-old woman was admitted to hospital a few hours after a rhinoplasty and cheek augmentation with hyaluronic acid, presenting with acute monocular vision loss in the right eye, retrobulbar pain, fatigue and vomiting. In addition, the patient presented a harbinger of impending skin necrosis and a complete oculomotor nerve palsy on the right side, choroidal ischemia and vision impairment. Supportive treatment and hyaluronidase injections into the ischemic tissue were initiated. A small scar at the tip of the nose, vision impairment and an irregular pupillary margin on the right side persisted at follow-up.These two case reports and the literature review emphasize the pathophysiological mechanisms leading to potentially devastating complications. In order to reduce the risk of vision loss secondary to cosmetic filler injections, practitioners should possess a thorough knowledge of anatomy and preventive strategies.The online version contains supplementary material available at 10.1186/s42466-022-00203-x. There is an ongoing expansion of the cosmetic armamentarium promising rapid and simple facial rejuvenation with long-lasting results. A growing number of cosmetic procedures are performed by non-medical practitioners, suggesting risk-freeness. In addition, easy access to the procedures can outweigh judgement of the level of qualification necessary to conduct these interventions safely. Risks might be greater than anticipated by the persons undergoing such procedures . InformeA broad spectrum of complications, ranging from mild or transient adverse events to permanent vision loss, is associated with these procedures, as illustrated by two case reports of complications following facial hyaluronic acid filler injections.A 43-year-old woman was admitted to hospital with monocular vision loss of the left eye in close temporal relation to a hyaluronic acid (HA) filler injection into the glabellar area. Immediately after receiving the injection, the patient perceived an abrupt fogging to complete blackness of the left eye as well as increased pressure and pain. The non-medical practitioner initiated an ocular massage and waited for the symptoms to resolve. However, her symptoms did not improve, and the patient presented to the emergency room one hour later accordingly. The ophthalmological examination revealed a left relative afferent pupillary defect (RAPD). On funduscopic examination, the optic disc showed sharp margins, thinned arteries and retinal whitening. Initially, no conjunctival injections or vitreous opacities were identified. An ophthalmic artery occlusion (OAO) was diagnosed upon identification of a “cherry-red spot”. The patient was referred to our neurology department to evaluate further treatment options. On initial examination 3.5 h after the onset of symptoms, the patient presented with monocular vision loss of the left eye, ptosis, ophthalmoplegia, periocular pain and nausea, cutaneous changes of the glabella region and forehead and sensory impairment in the left maxillary branch dermatome (V2). Peripheral reflexes and the remainder of the neurological and physical examinations were normal. She was devoid of a past medical history and was not taking any medications. She complained that the injecting non-medical practitioner  [The initiated brain MRI revealed an ischemic optic neuropathy on the left side without evidence of a thrombosis in the cavernous sinus Figs. , 2. TherDue to a lack of available evidence-based treatments, two supportive therapies consisting of either off-label use of intravenous thrombolysis with recombinant tissue plasminogen activator (rtPA) or the administration of aspirin, heparin, and systemic steroids were discussed in detail with the patient. The risk–benefit ratio of administering systemic thrombolysis was outlined, including the risk of symptomatic haemorrhage at the impending skin necrosis sites culminating in facial scar formation. An alternative and more conservative treatment option was offered, including aspirin, steroids, and heparin combined with local treatment, as this had been shown to result in a slight improvement in some cases . The patFollow-up MRIs of the head performed 1.5 weeks and two months after presentation corresponded to stable MRI findings Figs. , 4. TherA 29-year-old woman was admitted to hospital several hours after a rhinoplasty and cheek augmentation with HA. Immediately after receiving the injection, the patient experienced acute monocular vision loss in the right eye, retrobulbar pain, fatigue, and vomiting. Initial examination of the patient revealed a harbinger of impending skin necrosis, as shown in Fig. A brain MRI, fluorescein angiography and further interdisciplinary consultations including neurology, ophthalmology, and dermatology were performed.The patient was treated with systemic antibiotic therapy with ampicillin/sulbactam, high-dose methylprednisolone and low molecular weight heparin. In addition, several hyaluronidase injections into the ischemic tissue, including the nose and forehead, and sub-conjunctival dexamethasone injections were administered over the following days.The long-term follow-up revealed that the skin changes and eye perfusion abnormalities were only partially resolved. A small scar at the tip of the nose, vision impairment  and an irregular pupillary margin on the right side persisted.The high molecular weight polysaccharide HA was first described in 1934 by Karl Meyer and John Palmer in bovine vitreous humour . Since tInjectable dermal fillers for cosmetic indications, i.e. noninvasive facial rejuvenation, are very popular and commonly considered safe. Their availability and composition may explain their popularity. However, potential devastating complications and long-term sequelae may be underestimated.Von Bahr published a case report on multiple embolisms in the fundus of the eye after injection into the scalp with hydrocortisone in 1963, stating that “retinal ischemia secondary to embolism is so rare that the risk is not generally realized by doctors” . MeanwhiOn behalf of a national survey by the Korean Retina Society on iatrogenic vascular occlusion due to cosmetic filler injections, and based on fundus photographs and angiographic findings, ophthalmic artery occlusion was classified into six types: (1) ophthalmic artery occlusion (OAO), as diagnosed in Case 1, (2) generalized posterior ciliary artery occlusion (PCAO) with relative central retinal artery sparing, (3) central retinal artery occlusion (CRAO), (4) localized PCAO, (5) branch retinal artery occlusion, and (6) posterior ischemic optic neuropathy (PION) .The previously healthy 43-year-old woman in Case 1 reported a sudden and permanent vision loss immediately after HA injection, presuming a purely mechanical vascular obstruction. While the diagnostic path from a neurological point of view is normally based on detecting large artery atherosclerosis, embolism  or inflammatory vascular disease leading to interrupted blood flow to the retina, the exact underlying pathophysiology of HA complications often remains a conundrum.The predominant hypothesis is based on a retrograde flow of HA filler supported by accidental cannulation of the supratrochlear artery  with an injection pressure exceeding systolic pressure, followed by a subsequent anterograde flow of HA filler leading to vascular obstruction –17. The Beleznay et al. proposed that the associated ptosis results from reduced blood supply to the levator palpebrae superior muscle or its innervating nerves, while ophthalmoplegia could also be caused by the mere obstruction of the blood supply to the extraocular muscles . In addiIn our case, the patient presented with additional sensory impairment in the left maxillary branch dermatome (V2). From the neurologist’s perspective, the first differential diagnosis included a potential cavernous-sinus involvement, explaining the affected constellation of the vasa nervosa of the oculomotor nerve, the trochlear nerve, the abducens nerve, and the maxillary nerve. However, this would not explain the absence of involvement of the ophthalmic branch (V1), which also traverses through the lateral wall of the cavernous sinus.An alternative explanation for the involvement of the left V2 dermatome would be a retrograde embolism, distributed from the injection site at the glabella into the infraorbital artery located in anatomical proximity to the V2 branch of the infraorbital foramen.Additional studies have shown that the vessel volume of the supratrochlear artery from the glabella to the orbital apex is 85 µL, and one should not inject more than this critical volume as a bolus . Non-invTransient experimental occlusion of the central artery of the retina in rhesus monkeys lasting up to 98 min did not produce significant permanent neural damage. However, occlusion for 105 min or longer resulted in irreversible damage underlining the urgency of initiating swift treatment . Kapoor Intra-arterial obstruction is regarded as the main reason for skin necrosis –36, meanEven though the efficacy and safety of aspirin and heparin have not been proven in hyaluronic acid related occlusions, studies have highlighted their potential of preventing thrombotic spreading , 37–42. While Murray et al. urge clinicians to consider potential risks of instituting steroid therapy, i.e. impaired wound healing and increased risk of infection , most stThe Aesthetic Complications Expert (ACE) Group identified several key preventive strategies aimed at minimizing the risk of vision loss secondary to cosmetic filler injections, including a careful low injection pressure, injections of small increments and aspiration . Non-medAdditional file 1: Suggestions for potential medical standard operating procedures.


Renal transplantation from hepatitis C (HCV) nucleic acid amplification test-positive (NAAT-positive) donors to uninfected recipients has greatly increased the organ donation pool. However, there is concern for adverse outcomes in these recipients due to dysregulated immunologic activation secondary to active inflammation from acute viremia at the time of transplantation. This includes increased rates of cytomegalovirus (CMV) DNAemia and allograft rejection. In this study, we evaluate transcriptional responses in circulating leukocytes to define the character, timing, and resolution of this immune dysregulation and assess for biomarkers of adverse outcomes in transplant patients. We enrolled 67 renal transplant recipients  and performed RNA sequencing on serial samples from one, 3-, and 6-months post-transplant. CMV DNAemia and allograft rejection outcomes were measured. Least absolute shrinkage and selection operator was utilized to develop gene expression classifiers predictive of clinical outcomes. Acute HCV incited a marked transcriptomic response in circulating leukocytes of renal transplant recipients in the acute post-transplant setting, despite the presence of immunosuppression, with 109 genes significantly differentially expressed compared to controls. These HCV infection-associated genes were reflective of antiviral immune pathways and generally resolved by the 3-month timepoint after sustained viral response (SVR) for HCV. Differential gene expression was also noted from patients who developed CMV DNAemia or allograft rejection compared to those who did not, although transcriptomic classifiers could not accurately predict these outcomes, likely due to sample size and variable time-to-event. Acute HCV infection incites evidence of immune activation and canonical antiviral responses in the human host even in the presence of systemic immunosuppression. After treatment of HCV with antiviral therapy and subsequent aviremia, this immune activation resolves. Changes in gene expression patterns in circulating leukocytes are associated with some clinical outcomes, although larger studies are needed to develop accurate predictive classifiers of these events. There is a large disparity between the available number of kidney organ donors and recipients on the transplant waitlist–patients spend years on hemodialysis waiting for a kidney. Hepatitis C (HCV) nucleic acid antigen positive (NAAT-positive) donors are therefore being transplanted at a rapidly escalating rate to uninfected recipients to increase organ availability. This is due to the significant advancements made in the development of direct acting antiviral medications for the treatment and cure of HCV. These medications offer many benefits including high potency, low side effect profile, and pan-genotypic coverage –3.de novo donor specific antibodies (DSA), allograft rejection, and reactivation of opportunistic viral infections, such as cytomegalovirus (CMV) [However, there is concern that active HCV viremia results in an inflammatory state at the time of transplant and this may have downstream immunologic effects on the transplant recipient. The full extent of such effects is still being clarified. There have been reports of increased incidence of adverse outcomes in HCV transplant recipients, including increased incidence of us (CMV) –7. TheseImmune activation can be measured and monitored in the peripheral blood at the transcriptomic level in the transplant recipient. It is known that chronic HCV viremia triggers unique transcriptomic and cytokine responses that clarify disease diagnosis, pathophysiology, and severity –10. ThisGene expression profiling has also been used to noninvasively detect rejection in solid organ transplantation –23. HoweAll study patients were enrolled after written informed consent at Duke University Medical Center (DUMC). The study was approved by the Institutional Review Board (IRB) at DUMC (Pro00106012) and was performed in accordance with the Declaration of Helsinki. Sixty-seven patients without HCV infection who underwent renal transplantation from July 2020 to August 2021 were enrolled at the time of renal transplantation, including subjects who received an organ from a donor who was HCV-NAAT-positive, as well as subjects who received an organ from a donor who was HCV-NAAT-negative (Controls).Whole blood was collected from all subjects in PAXgene Blood RNA Tubes (QIAGEN) for RNA sequencing and serum was collected for additional analysis. Each subject had planned sample collections at the first initial outpatient Infectious Diseases clinic visit just prior to the start of antiviral therapy (or corresponding timepoint for controls) at a median of 17 days post-transplant (IQR 15–22) (Timepoint 1); at the end of 12-weeks of antiviral therapy ; and at a sustained virologic response 12 weeks after treatment (SVR12) . DSA testing was performed using multianalyte bead assays  for all subjects at 6 months post-transplant.All HCV-NAAT-positive organ recipients were treated with 12 weeks of a direct-acting antiviral regimen–either ledipasvir-sofosbuvir, sofosbuvir-velpatasvir, or glecaprevir-pibrentasvir, per standard of care transplant management. Treatment regimens were chosen by the patient’s primary Transplant Infectious Diseases provider independently of the study, based on patient comorbidities, medication interactions, and insurance coverage. All antiviral regimens were started in the outpatient setting after index transplant hospitalization.At DUMC, low-risk CMV patients  are treated with universal prophylaxis for herpes simplex virus (HSV) with acyclovir only. In intermediate risk populations (R+), renal transplant recipients use pre-emptive prophylaxis with acyclovir for 90 days with weekly CMV polymerase chain reaction (PCR) monitoring if they received steroid induction, or universal prophylaxis for 180 days with valganciclovir if they received thymoglobulin. High risk CMV populations (D+/R-) are treated with universal CMV prophylaxis with valganciclovir for at least 180 days.® AmpliPrep/COBAS® TaqMan® CMV Test at DUMC). Patients with “clinical” CMV DNAemia were defined as those with equal or greater than 137 international units/milliliter (IU/mL) of CMV DNA present . Patients with “subclinical” CMV infection were defined as those that had detectable CMV DNA below 137 IU/mL (the lower limit of quantification of the PCR). Lastly, those patients with no detectable CMV DNA were termed the “no CMV” group.CMV DNAemia was detected and quantified using quantitative PCR  patients with any CMV infection  to patients with no CMV infection or (2) clinical CMV patients to patients with no CMV. For the comparison in (2), patients with subclinical CMV infection (less than 137 IU/mL) were excluded from analysis.Rejection was defined as both biopsy-proven allograft cellular rejection and the development of DSAs post-transplant in order to capture both cellular and antibody-mediated rejection outcomes.Upon collection, whole blood was stored at -80° Celsius (C) in PAXgene Blood RNA tubes. Total RNA was isolated and purified using the QIAGEN PAXgene Blood miRNA kit. Stranded cDNA libraries were prepared using the Nugen Universal Plus mRNA-seq kit with Globin AnyDeplete (Tecan Life Sciences) yielding stranded, poly(A)-selected, globin-depleted cDNA sequencing libraries. The cDNA libraries were sequenced by the Sequencing and Genomic Technologies Shared Resource within the Duke Center for Genomic and Computational Biology in two batches, using an Illumina NovaSeq 6000 instrument that generated 50bp paired-end reads. Sequences were aligned and quantified with Spliced Transcripts Alignment to a Reference (STAR), using the NCBI Homo sapiens GRCh38 genome as a reference .Following sequencing, alignment, and quantification, all subsequent data processing and analyses were performed using R . InitialComparison of clinical demographics was performed by chi-square test for categorical variables or Mann-Whitney for continuous variables.2-transformed mean-variance relationship. The within-patient correlation was estimated given the weights, and then the weights were recalculated using the correlation estimate. Upon fitting the linear models, p-values were adjusted using the false discovery rate (FDR) using the Benjamini-Hochberg method, and transcripts with an FDR ≤5% were considered differentially expressed.Differential expression analysis (DEA) was carried out using the limma package within R . A serieGSEA . was perLeast absolute shrinkage and selection operator (LASSO), available in the R package glmnet , was useOne hundred iterations of the following procedure were performed. The data  were subsampled so that for cases the most recent blood sample prior to the outcome was utilized. For CMV DNAemia, this meant the most recent sample prior to the detection of CMV. Any patient with samples taken only after detection of CMV DNAemia was removed from the corresponding analysis. For controls, a single, randomly selected blood sample was used. One-by-one each observation was held out, and three-fold cross-validation (CV) was used on the remaining observations to train a LASSO model. The training model was optimized in order to minimize the deviance, the probability of the corresponding outcome for the held-out observation was predicted using the tuning parameter value that minimized the deviance.These analyses focused on identifying transcripts associated with outcomes of interest and not on building prediction models. As with the predictive analysis, the 1,000 transcripts with the highest variance in expression were selected to build a predictive model. All subsequent models contained those 1,000 transcripts, as well as the RNA extraction batch.For each outcome, 500 iterations of a leave-one-out cross-validation (LOOCV) procedure were performed. As with the predictive analysis, the data were subsampled such that, for cases, the most recent blood sample prior to the corresponding outcome was used, and for controls a randomly sampled blood sample was used. LOOCV was used to select a value for the LASSO regression tuning parameter that minimized the deviance, and the coefficients corresponding to that tuning parameter were extracted to represent the final fit for that iteration.The transcriptomic signals for CMV DNAemia  and rejection were compared with those from publicly available datasets. For CMV DNAemia, we utilized a case-control study examining longitudinal changes in expression upon the onset of DNAemia, defined as CMV DNA concentrations >137 IU/mL (GEO Accession Number: GSE168598) . This stWe compared transcriptomic signals in our study associated with rejection with a prospective study focused on finding noninvasive biomarkers for acute cellular rejection . The com2-transformed counts per million for all transcripts were regressed on the outcomes using the R package limma, and p-values were adjusted for multiple testing using the FDR. The lists of transcripts exceeding different significance thresholds were compared. Furthermore, the log2 fold-changes and p-values for both comparative studies were plotted simultaneously to look for gross similarities.The results from DEA and GSEA were compared across studies. For DEA, the logWe enrolled 67 subjects following renal transplantation– 37 recipients of kidneys from HCV-NAAT-positive organ donors and 30 recipients of kidneys from HCV-NAAT-negative organ donors (Controls). Full demographics are described in CMV DNAemia was defined as any detectable level of CMV on quantitative PCR from peripheral blood. Twenty-nine subjects had evidence of CMV DNAemia during the study period  . These cde novo DSAs post-transplant– 8 subjects demonstrated evidence of acute cellular rejection (4 control subjects and 4 with HCV-infected donors) while 4 recipients had evidence of positive DSAs at approximately 6 months post-transplant (2 control subjects and 2 with HCV-infected donors) . The median time from sample collection to first detected CMV DNAemia  was -48 days (IQR -26 to -139). This was -47 days (IQR -25 to -89) for controls and -84 days for the HCV cohort (IQR -34 to -129) (p = 0.680). The median time from sample collection to clinical CMV DNAemia  was -49 days (IQR -26 to -92). This was -67 days for controls (IQR -44 to -90) and -42 days for the HCV cohort (IQR -23 to -106) (p = 0.558).To better understand the relationship between gene expression differences and specific adverse transplant clinical outcomes, we performed univariate analyses of subjects who developed CMV DNAemia post-transplant compared to those who did not, both in the presence and absence of HCV. To assess for the presence of a persistently dysregulated transcriptomic signal that begins prior to CMV DNAemia, continues to be present over time, and is conserved both in the presence and absence of HCV, we first reviewed differential gene expression data in all subjects at all timepoints within each group . In the transplant recipients who developed CMV DNAemia , 995 genes were upregulated and 974 genes were downregulated, compared to those who did not develop CMV. When the CMV DNAemia cutoff was increased to >137 IU/mL , the number of upregulated genes increased to 1,187, while 808 genes were downregulated. This included genes involved in immune activation and T cell signaling, including biological processes such as ‘T cell immunoreceptor,’ ‘T cell receptor gamma,’ ‘Fc receptor,’ and ‘lymphocyte activation.’ 335 of those genes overlapped with published CMV response patterns in renal transplant recipients .To identify specific HCV-associated differences in the immune response to CMV infection, we next examined the differential gene expression data of transplant recipients who developed CMV DNAemia  in the control cohort (n = 15 subjects) compared to the HCV-infected cohort (n = 14 subjects). In the control group, 52 genes were upregulated and 53 were downregulated. In the HCV cohort, this number was much lower, with only 5 genes upregulated and 1 downregulated. We inspected these gene expression differences separately at each individual post-transplant timepoint. However, in both the HCV and control groups, no genes were significantly differentially expressed at any timepoint.We next examined the data for transcriptomic signatures that are predictive of CMV DNAemia after transplantation. A LASSO regression model was repeatedly fit to samples of the original data. For patients that exhibited CMV DNAemia , the blood sample taken prior to the detection of CMV was used, and for patients that did not exhibit any CMV DNAemia, a random blood sample was used. Patients with no blood samples prior to detection of CMV were removed from the analysis. The transcripts selected in the predictive modeling are summarized in The trained LASSO models were used to predict CMV DNAemia, and the performance of those predictions was measured using the area under a receiver operating characteristic (AUROC). We found that there were marked differences in the predicted probabilities of CMV DNAemia between those transplant recipients who developed CMV DNAemia and those who did not in multiple modeling iterations,  by excluding any subject with CMV levels that were less than 137 IU/mL, as an elevated quantitative PCR is more indicative of clinically meaningful CMV infection. However, due to low number of subjects with the outcome of clinical CMV DNAemia (n = 9 subjects), when subclinical CMV subjects were removed from the dataset the smaller sample size precluded the development of significant predictors. Thus, we additionally examined further associations between this outcome and the expression of transcripts with the highest variance. We performed 500 iterations of a procedure in which we subsampled the data in the same manner as the predictive analysis. We used LOOCV to identify a tuning parameter value for LASSO regression that minimized the deviance and then fit a final model for each iteration using that value. Ultimately, 57 transcripts were selected in 1% or more of the fitted models. The majority of the transcripts (47) were consistently negative , and theIn order to understand how these results compare to published data, we next examined an independent dataset of gene expression changes in response to CMV DNAemia over time in renal transplantation (in the absence of HCV infection) by Ahn, et al . We perfGiven fixed sample collection times, samples were often not available at or near the time of the event of interest . The median time from sample collection to rejection (for samples collected prior to the outcome only), was -103 days (IQR -79 to -197)–for controls this was -182 days (IQR -106 to -244). For the HCV cohort, this was -91 days (IQR -47 to -146) (p = 0.224).To further understand the relationship between sequelae of HCV infection and rejection, we performed univariate analyses of subjects with allograft rejection compared to controls, both in the presence and absence of HCV. To assess for the presence of conserved but persistently dysregulated transcriptomic signals that begin prior to rejection and do not resolve after treatment, we first grouped differential gene expression data in all subjects at all timepoints . In the differential expression analysis of all subjects at all timepoints, no genes were significantly up or downregulated in response to allograft rejection. When transplant recipients with rejection were divided by HCV status, differential expression analysis of the control population revealed that 6 genes were significantly upregulated and none were downregulated  in response to allograft rejection . In the HCV group, only 1 gene was significantly downregulated, cystatin F (CST7), while no genes were upregulated. Upregulation of this gene has been previously identified as a biomarker of acute inflammation . When th2FC of 3.95, followed by the 6-month timepoint  with maximum log2FC of 2.85. Interestingly, these changes appear to be specific to HCV-infected individuals, as they were not seen when examining rejection in the HCV-negative cohort.On univariate comparison of allograft rejection compared to no rejection in the HCV cohort alone, we found that the most quantitatively upregulated genes included multiple immunoglobulin markers, including IGHA2, IGKV3, IGHG1, IGHA1, IGLC2, IGHG2, IGKC. These immunoglobulin genes were not as notably expressed at the acute timepoint but were most strongly expressed at the 3-month timepoint  with maximum logIn our predictive analysis the data were subset, where the majority were used to train models in which we constructed relationships between the expression data and future allograft rejection. Each trained model was then used to predict a single future occurrence of allograft rejection. Attempts at predicting future allograft rejection were largely unsuccessful though, in part due to the low proportion of patients that experienced rejection, causing predictions of rejection to be consistently biased  by Zhang, et al . On diffAt the pathway level, similar results were seen. GSEA analysis of HCV-NAAT-positive patients with rejection (regardless of sample timing), demonstrated genes involved in the immune pathways, including ‘immunoglobulin complex,’ ‘humoral response,’ ‘B cell mediated immunity,’ ‘phagocytosis,’ ‘defense response to virus,’ and ‘complement activation.’ However, this was distinct from the GSEA analysis in the Zhang, et al., study where involved genes were more consistent with routine cellular functions, as opposed to specialized immunologic functions.We next applied the transcriptomic signature of subclinical rejection from the Zhang, et al., paper to our HCV allograft dataset (using only samples obtained prior to the rejection event). However, when the independent 17-gene signature of rejection was applied to our data, the signature was unable to clearly differentiate between allograft rejection and controls in both the uninfected donor and HCV donor datasets  . There wFor allograft rejection, gene expression differences were not as distinct between the HCV and control cohorts. We were not able to identify notable differences in gene expression levels between those transplant recipients who developed allograft rejection and those who did not, again limiting our ability to identify a predictive signature for this clinical outcome. This is likely due to the variable and often large time between sample collection and rejection event (median -103 days) and may also be impacted by the relatively small sample size. Within the HCV cohort, only one gene was significantly downregulated at early post-transplant timepoints between those who would later develop rejection and those who would not, cystatin F (CST7), while no genes were upregulated. Upregulation of this gene has been previously identified as a biomarker of acute inflammation . One hypde novo DSA production. These immunoglobulin genes were not as notably expressed in the HCV-negative cohort, suggesting a role of HCV infection in progressing this pathologic process. This suggests that the presence of HCV infection may trigger B-cell focused hyperactivation, leading to an environment where maladaptive immunologic outcomes, such as DSA production, may occur. Unfortunately, our study did not analyze specific B cell subsets that were exhibiting these changes. Additional studies (including looking at larger HCV datasets or later timepoints) are necessary to determine the target specificity of those B cells undergoing hyperactivation.When examining the magnitude of gene expression changes in the setting of rejection compared to no rejection in the HCV cohort, we found that the most upregulated genes included multiple immunoglobulin markers, including IGHA2, IGKV3, IGHG1, IGHA1, IGLC2, IGHG2, IGKC. When examining this subgroup by individual timepoints, these immunoglobulin genes were not as notably expressed at the acute timepoint, but were most strongly expressed at the 3-month timepoint, followed by the 6-month timepoint. This aligns with the known delay from transplant to rejection, particularly of antibody-mediated responses in the setting of We attempted to validate these rejection data with an external gene expression dataset of renal transplant recipients . HoweverIn conclusion, this work reveals novel components of the character and timing of immune responses to HCV transplantation as seen through the lens of transcriptional biomarkers in circulating leukocytes. We found that HCV infection produces a robust inflammatory response in the acute setting after transplant. This could prove to be maladaptive given the strong early signal despite the presence of systemic immunosuppression and association with adverse clinical outcomes. Gene expression differences are also seen in response to CMV DNAemia, although these findings will need to be studied in larger cohorts. The feasibility of this signal as a predictive clinical biomarker appears to be clouded by the additional overwhelming HCV response. Encouragingly, this signal improves and even resolves with HCV treatment and subsequent aviremia, supporting the practice of early HCV treatment.S1 File(DOCX)Click here for additional data file.


A 59-year-old male was initially diagnosed with pemphigus Vulgaris and received rituximab after a suboptimal clinical response with low-dose steroids and cyclophosphamide. Shortly after the third dose, he had acute interstitial pneumonia which was attributed to rituximab as there were no signs of any infective etiology after a detailed workup. He was put on mechanical ventilation but the dramatic response to pulse steroids helped the patient in early extubation and a favorable outcome. Rituximab is a relatively newer immunosuppressive drug that acts against the CD20 antigen of the B cells and hence is used usually in disorders where B cells play a predominant role . Common 2 92% with 8 liters of oxygen via face mask, pulse rate was 130/min, BP 130/80 mm of Hg, multiple discrete plaques few with crust present over face, chest, and back, and multiple hyperpigmented macules over the chest and back consistent with background diagnosis of pemphigus Vulgaris. On auscultation bilateral inframammary, infra-axillary, and infra-scapular fine inspiratory crepitations were present. A high-resolution CT scan was done and showed diffuse consolidation and Ground Glass Opacities (GGOs) with interlobar septal thickening in bilateral lung fields with honeycombing in bilateral lower lobes , aerobic, anaerobic, fungal, and viral organisms. Broncho alveolar lavage (BAL) fluid analysis though showed 90% lymphocytes. After thorough investigations, rituximab-induced ILD was suspected as a diagnosis of exclusion and temporal association of the symptoms/signs with the treatment of rituximab. After a detailed discussion, the patient was started on pulse steroids 500 mg methylprednisolone for three days followed by a 40 mg methylprednisolone maintenance dose. The patient improved significantly in terms of oxygen requirement and required pressure support and hence was extubated after four days of starting high-dose steroids which further supports the diagnosis of rituximab-ILD. The patient was discharged in a stable condition after two weeks of hospitalization but was lost to follow-up thereafter.A 59-year-old male was diagnosed with pemphigus Vulgaris in February 2022. The patient was initiated on low-dose steroids, cyclophosphamide 50 mg once a day initially by the dermatologist but because of progressive disease, he has escalated to rituximab 500 mg for three doses. Because of a history of previous treatment for pulmonary tuberculosis and occasional episodes of dry cough, a pulmonary consultation was sought and Computed Tomography (CT) scan of the thorax was advised as chest radiograph and pulmonary function tests were inconclusive. High-resolution CT revealed right-sided bronchiectasis and basal reticulations and subtle honeycombing changes Figure , which es Figure . ConsideRituximab is a chimeric G1-k monoclonal antibody channeled against a CD20 antigen present on the surface of B cells but not on plasma cells and stem cells. This makes it more selective towards the disorders caused by B cells and it also acts synergistically by accelerating apoptosis caused by antineoplastic drugs . Liote eMost of the available literature has reported rituximab-induced ILD in patients without prior lung injury except for one case reported by Vulsteke et al., which reported worsening of underlying ILD with rituximab, almost similar to our case . The casDiagnosis mostly rests on the temporal association of rituximab with the onset of symptoms/radiological findings. Because all patients receiving rituximab have already underlying immunosuppression, it is prudent to rule out an infectious etiology by doing all necessary investigations. CT findings include GGOs along with focal/diffuse alveolar infiltrates and rarely a macronodular pattern . Our patTreatment of rituximab-induced ILD is supplement oxygen, methylprednisolone, and supportive ventilation ,6. The dIn conclusion, rituximab-induced ILD is a rare but misdiagnosed and near fatal condition that can be treated with a high index of clinical suspicion. Because its symptoms and radiological findings are nonspecific, early diagnosis and treatment are the keys for this entity in preventing morbidity and mortality after ruling out infection which is the most common differential diagnosis.