Intrinsic Subset Research Articles

The Changes in the Quantity of Lymphocyte Subpopulations during the Process of Sepsis.

Sepsis remains a global challenge, especially in low- and middle-income countries, where there is an urgent need for easily accessible and cost-effective biomarkers to predict the occurrence and prognosis of sepsis. Lymphocyte counts are easy to measure clinically, and a large body of animal and clinical research has shown that lymphocyte counts are closely related to the incidence and prognosis of sepsis. This review extensively collected experimental articles related to lymphocyte counts since the unification of the definition of sepsis. The article categorizes and discusses the relationship between absolute lymphocyte counts, intrinsic lymphocyte subsets, effector T-lymphocytes, B-lymphocytes, dendritic cells, and the incidence and prognosis of sepsis. The results indicate that comparisons of absolute lymphocyte counts alone are meaningless. However, in addition to absolute lymphocyte counts, innate lymphocyte subsets, effector T-cells, B-lymphocytes, and dendritic cells have shown certain research value in related studies.

Abstract 3126: Multimodal genome-wide survey of progressing and non-progressing DCIS

Abstract Introduction: Our current understanding of the molecular changes predisposing Ductal carcinoma in situ (DCIS) to progress to invasive breast cancer (IBC) is limited. The aim of this study is to characterize the molecular changes of DCIS to improve DCIS risk assessment. Methods: We obtained paraffin-embedded tissue of 210 DCIS samples with no concurrent or antecedent IBC from 5 cancer institutions, and extracted DNA and RNA. Transcriptome analysis: Illumina’s TruSeq RNA Exome kit was used for library construction, followed by sequencing. Methylome analysis: Bisulfite treated genomic DNA was restored and arrayed using the Illumina 450K methylation chips. DNA Copy number (CNV) analysis: CNV was estimated from the methylation data set using the EpiCopy R package. Results: Samples passing Q/C metrics: Transcriptome: 59 cases, 63 controls. Methylome & CNV: 93 cases, 98 controls. We classified samples into their intrinsic PAM50 subsets. Cases showed an increase in the Her2 subtype, and the controls were enriched in LuminalA (LumA) samples, while Basal and LuminalB (LumB) subtypes were evenly distributed. Compared to the TCGA IBC dataset, proportions of Her2 and LumB subtypes in DCIS were increased while the LumA cohort was decreased. Unsupervised clustering of the transcriptome data resulted in 3 clusters with key differences between PAM50 subtypes: one cluster predominated in Basal and Her2 subtypes, and a second was enriched in hormone-positive samples (LumA and LumB). For the methylome data, the optimal number of clusters was 6. Again, several clusters showed correlation with PAM50 subtype, e.g., one was enriched for hormone-negative subtypes (Basal and Her2), while another was enriched for hormone-positive subtypes (LumA and LumB). For the CNV data, the optimal number of clusters was 4. Here, one CNV cluster appeared predominantly in the Basal subtype, and there were multiple regions showing significant subtype-specific differences with the TCGA IBC data set. While the well-known heterogeneity of DCIS prevents the above broad molecular categories from strongly stratifying DCIS by outcome, we are continuing to analyze our data sets for predictive molecular signatures. Since the long-term outcome of all DCIS in this cohort is known, we are in the unique position to pursue additional questions such as PAM50 subtype differences in times-to-events or lineage fidelity (i.e., whether the subtype of the subsequent IBC matches the preceding DCIS), and it appears that the time to IBC diagnosis is shortest in Basal DCIS, while at the same time, lineage fidelity is lowest in Basal DCIS, as has been previously reported. Conclusions: Our subtype-stratified analyses identified multiple molecular differences both between intrinsic subtypes as well as between DCIS and IBC that suggest subtype-specific characteristics that may be exploitable for risk stratification of DCIS. Citation Format: Marija Debeljak, Soonweng Cho, Bradley Downs, Michael Considine, Brittany Avin-McKelvey, Yongchun Wang, William Grizzle, Katherine Hoadley, Charles Lynch, Brenda Hernandez, Paul van Diest, Wendy Cozen, Ann Hamilton, Debra Hawes, Edward Gabrielson, Ashley Cimino-Mathews, Liliana Florea, Leslie Cope, Christopher B. Umbricht. Multimodal genome-wide survey of progressing and non-progressing DCIS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3126.

Cronos: A Machine Learning Pipeline for Description and Predictive Modeling of Microbial Communities Over Time.

Microbial time-series analysis, typically, examines the abundances of individual taxa over time and attempts to assign etiology to observed patterns. This approach assumes homogeneous groups in terms of profiles and response to external effectors. These assumptions are not always fulfilled, especially in complex natural systems, like the microbiome of the human gut. It is actually established that humans with otherwise the same demographic or dietary backgrounds can have distinct microbial profiles. We suggest an alternative approach to the analysis of microbial time-series, based on the following premises: 1) microbial communities are organized in distinct clusters of similar composition at any time point, 2) these intrinsic subsets of communities could have different responses to the same external effects, and 3) the fate of the communities is largely deterministic given the same external conditions. Therefore, tracking the transition of communities, rather than individual taxa, across these states, can enhance our understanding of the ecological processes and allow the prediction of future states, by incorporating applied effects. We implement these ideas into Cronos, an analytical pipeline written in R. Cronos’ inputs are a microbial composition table (e.g., OTU table), their phylogenetic relations as a tree, and the associated metadata. Cronos detects the intrinsic microbial profile clusters on all time points, describes them in terms of composition, and records the transitions between them. Cluster assignments, combined with the provided metadata, are used to model the transitions and predict samples’ fate under various effects. We applied Cronos to available data from growing infants’ gut microbiomes, and we observe two distinct trajectories corresponding to breastfed and formula-fed infants that eventually converge to profiles resembling those of mature individuals. Cronos is freely available at https://github.com/Lagkouvardos/Cronos.

A genomic meta-analysis of clinical variables and their association with intrinsic molecular subsets in systemic sclerosis.

Four intrinsic molecular subsets (inflammatory, fibroproliferative, limited, normal-like) have previously been identified in SSc and are characterized by unique gene expression signatures and pathways. The intrinsic subsets have been linked to improvement with specific therapies. Here, we investigated associations between baseline demographics and intrinsic molecular subsets in a meta-analysis of published datasets. Publicly available gene expression data from skin biopsies of 311 SSc patients measured by DNA microarray were classified into the intrinsic molecular subsets. RNA-sequencing data from 84 participants from the ASSET trial were used as a validation cohort. Baseline clinical demographics and intrinsic molecular subsets were tested for statistically significant associations. Males were more likely to be classified in the fibroproliferative subset (P = 0.0046). SSc patients who identified as African American/Black were 2.5 times more likely to be classified as fibroproliferative compared with White/Caucasian patients (P = 0.0378). ASSET participants sera positive for anti-RNA pol I and RNA pol III autoantibodies were enriched in the inflammatory subset (P = 5.8 × 10-5, P = 9.3 × 10-5, respectively), while anti-Scl-70 was enriched in the fibroproliferative subset. Mean modified Rodnan Skin Score (mRSS) was statistically higher in the inflammatory and fibroproliferative subsets compared with normal-like (P = 0.0027). The average disease duration for inflammatory subset was less than fibroproliferative and normal-like intrinsic subsets (P = 8.8 × 10-4). We identified multiple statistically significant differences in baseline demographics between the intrinsic subsets that may represent underlying features of disease pathogenesis (e.g. chronological stages of fibrosis) and have implications for treatments that are more likely to work in certain SSc populations.

Blood Genomics Identifies Three Subtypes of Systemic Lupus Erythematosus: "IFN-High," "NE-High," and "Mixed".

Purpose Systemic lupus erythematosus (SLE) is a systemic and multifactorial autoimmune disease, and its diverse clinical manifestations affect molecular diagnosis and drug benefits. Our study was aimed at defining the SLE subtypes based on blood transcriptome data, analyzing functional patterns, and elucidating drug benefits. Methods Three data sets were used in this paper that were collected from the Gene Expression Omnibus (GEO) database, which contained two published data sets of pediatric and adult SLE patients (GSE65391, GSE49454) and public longitudinal data (GSE72754) from a cohort of SLE patients treated with IFN-α Kinoid (IFN-K). Based on disease activity scores and gene expression data, we defined a global SLE signature and merged three clustering algorithms to develop a single-sample subtype classifier (SSC). Systematic analysis of coexpression networks based on modules revealed the molecular mechanism for each subtype. Results We identified 92 genes as a signature of the SLE subtypes and three intrinsic subsets (“IFN-high,” “NE-high,” and “mixed”), which varied in disease severity. We speculated that IFN-high might be due to the overproduction of interferons (IFNs) caused by viral infection, leading to the formation of autoantibodies. NE-high might primarily result from bacterial and fungal infections that stimulated neutrophils (NE) to produce neutrophil extracellular traps (NETs) and induced individual autoimmune responses. The mixed type contained both of these molecular mechanisms and showed an intrinsic connection. Conclusions Our research results indicated that identifying the molecular mechanism associated with different SLE subtypes would benefit the molecular diagnosis and stratified therapy. Moreover, repositioning of IFN-K based on subtypes also revealed an improved therapeutic effect, providing a new direction for disease treatment and drug development.

Machine learning predicts stem cell transplant response in severe scleroderma

ObjectiveThe Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated clinical benefit of haematopoietic stem cell transplant (HSCT) compared with cyclophosphamide (CYC). We mapped PBC (peripheral blood cell) samples from the SCOT...

Regulator combinations identify systemic sclerosis patients with more severe disease.

Systemic sclerosis (SSc) is a heterogeneous autoimmune disorder that results in skin fibrosis, autoantibody production, and internal organ dysfunction. We previously identified 4 “intrinsic” subsets of SSc based upon skin gene expression that are found across organ systems. Gene expression regulators that underlie the SSc-intrinsic subsets, or are associated with clinical covariates, have not been systematically characterized. Here, we present a computational framework to calculate the activity scores of gene expression regulators and identify their associations with SSc clinical outcomes. We found that regulator activity scores can reproduce the intrinsic molecular subsets, with distinct sets of regulators identified for inflammatory, fibroproliferative, limited, and normal-like samples. Regulators most highly correlated with modified Rodnan skin score (MRSS) also varied by intrinsic subset. We identified subgroups of patients with fibroproliferative and inflammatory SSc with more severe pathophenotypes, such as higher MRSS and increased likelihood of interstitial lung disease (ILD). Using an independent cohort, we show that the group with more severe ILD was more likely to show forced vital capacity decline over a period of 36–54 months. Our results demonstrate an association among the activation of regulators, gene expression subsets, and clinical variables that can identify patients with SSc with more severe disease.

Mast Cell Activation in the Systemic Sclerosis Esophagus.

Previously, we discovered similar esophageal gene expression patterns in patients with systemic sclerosis (SSc) and eosinophilic esophagitis (EoE) where eosinophil/mast cell-targeted therapies are beneficial. Because SSc and EoE patients experience similar esophageal symptoms, we hypothesized that eosinophil/mast cell-directed therapy may potentially benefit SSc patients. Herein, we determine the association between esophageal mast cell quantities, gene expression and clinical parameters in order to identify SSc patients who may benefit from eosinophil/mast cell-directed therapy. Esophageal biopsies from SSc patients and healthy participants were stained for tryptase, a mast cell marker, and associations with relevant clinical parameters including 24h esophageal pH testing were assessed. Intra-epithelial mast cell density was quantified by semi-automated microscopy. Microarray data were utilized for functional and gene set enrichment analyses and to identify intrinsic subset (IS) assignment, an SSc molecular classification system that includes inflammatory, proliferative, limited and normal-like subsets. Esophageal biopsies from 40 SSc patients (39 receiving proton pump inhibition) and eleven healthy participants were studied. Mast cell numbers in both the upper esophagus (rs = 0.638, p = 0.004) and the entire (upper + lower) esophagus (rs = 0.562, p = 0.019) significantly correlated with acid exposure time percentage. The inflammatory, fibroproliferative, and normal-like ISs originally defined in skin biopsies were identified in esophageal biopsies. Although esophageal mast cell numbers in SSc patients and healthy participants were similar, gene expression for mast cell-related pathways showed significant upregulation in the inflammatory IS of SSc patients compared to patients classified as proliferative or normal-like. Esophageal mast cell numbers are heterogeneous in SSc patients and may correlate with acid exposure. Patients with inflammatory IS profiles in the esophagus demonstrate more tryptase staining. Mast cell targeted therapy may be a useful therapeutic approach in SSc patients belonging to the inflammatory IS, but additional studies are warranted.

Tumor Heterogeneity and Phenotypic Plasticity in Bladder Carcinoma

Bladder carcinoma is the most common malignancy of the urinary system with a high recurrence rate. As of today, bladder carcinoma does not sufficiently benefit from new therapeutic strategies. The molecularly heterogeneous landscape contributes an enormous challenge in the management of this cancer. Consensus clustering based on gene expression data from muscle-invasive and non-muscle-invasive tumors identified multiple intrinsic molecular subsets of this cancer. In our recent study, we have computed epithelial-mesenchymal transition (EMT) scores of three key bladder cancer subtypes followed by the comparative phosphoproteomics profiling of the molecular subtypes. The most aggressive non-type bladder subtype correlated with a mesenchymal-like phenotype. The improved stratification of the molecular features of these subtypes would provide a new opportunity for a deeper understanding of disease pathogenesis. This review focuses on the concepts of cellular plasticity and heterogeneity in bladder carcinoma. We also raised some of the challenges during the discovery and therapeutic intervention of targeted therapy and its clinical implication.

A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets.

High-throughput gene expression profiling of tissue samples from patients with systemic sclerosis (SSc) has identified 4 "intrinsic" gene expression subsets: inflammatory, fibroproliferative, normal-like, and limited. Prior methods required agglomerative clustering of many samples. In order to classify individual patients in clinical trials or for diagnostic purposes, supervised methods that can assign single samples to molecular subsets are required. We undertook this study to introduce a novel machine learning classifier as a robust accurate intrinsic subset predictor. Three independent gene expression cohorts were curated and merged to create a data set covering 297 skin biopsy samples from 102 unique patients and controls, which was used to train a machine learning algorithm. We performed external validation using 3 independent SSc cohorts, including a gene expression data set generated by an independent laboratory on a different microarray platform. In total, 413 skin biopsy samples from 213 individuals were analyzed in the training and testing cohorts. Repeated cross-fold validation identified consistent and discriminative markers using multinomial elastic net, performing with an average classification accuracy of 87.1% with high sensitivity and specificity. In external validation, the classifier achieved an average accuracy of 85.4%. Reanalyzing data from a previous study, we identified subsets of patients that represent the canonical inflammatory, fibroproliferative, and normal-like subsets. We developed a highly accurate classifier for SSc molecular subsets for individual patient samples. The method can be used in SSc clinical trials to identify an intrinsic subset on individual samples. Our method provides a robust data-driven approach to aid clinical decision-making and interpretation of heterogeneous molecular information in SSc patients.

Different patterns of pelvic ureteral endometriosis. What is the best treatment? Results of a retrospective analysis.

Endometriosis is an estrogendependent disease. The incidence of urinary tract endometriosis (UE) increased during the last few years and, nowadays, it ranges from 0.3 to 12% of all women affected by the disease. The ureter is the second most common site affected. The ureteral endometriosis is classified in extrinsic and intrinsic. The aim of this study is to individuate the best treatments for each subset of ureteral endometriosis. 32 patients diagnosed with surgically treated UE were retrospectively reviewed. The patients were divided into 3 subsets (intrinsic UE, extrinsic UE with and without obstruction). The patients with intrinsic UE (n = 10) were treated with laser endoureterotomy. The patients with extrinsic UE (n = 22) were divided in two subsets with (n = 16) and without (n = 6) hydronephrosis. All the patients underwent ureteral stenting, and resection and reimplantation was performed in the first group, and when the mass was > 2.5 cm (n = 3) Boari flap was performed. Laparoscopic ureterolysis (shaving) was performed in the second group. In the extrinsic subset of UE, we obtained an high therapeutic success (84%). Conversely, in the intrinsic subset there was a recurrence rate of the disease in 6/10 of the patients (60%). Ureterolysis seems to be a good treatment in extrinsic UE without obstruction. Resection and reimplantation allows excellent results in the extrinsic UE with obstruction. In the intrinsic subset, the endoureterotomy approach is inadequate.

Identification of Optimal Mouse Models of Systemic Sclerosis by Interspecies Comparative Genomics

ObjectiveUnderstanding the pathogenesis of systemic sclerosis (SSc) is confounded by considerable disease heterogeneity. Animal models of SSc that recapitulate distinct subsets of disease at the molecular level have not been delineated. We applied interspecies comparative analysis of genomic data from multiple mouse models of SSc and patients with SSc to determine which animal models best reflect the SSc intrinsic molecular subsets.MethodsGene expression measured in skin from mice with sclerodermatous graft‐versus‐host disease (GVHD), bleomycin‐induced fibrosis, Tsk1/+ or Tsk2/+ mice was mapped to human orthologs and compared to SSc skin biopsy–derived gene expression. Transforming growth factor β (TGFβ) activation was assessed using a responsive signature in mice, and tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression was measured in SSc patient and mouse skin.ResultsGene expression in skin from mice with sclerodermatous GVHD and bleomycin‐induced fibrosis corresponded to that in SSc patients in the inflammatory molecular subset. In contrast, Tsk2/+ mice showed gene expression corresponding to the fibroproliferative SSc subset. Enrichment of a TGFβ‐responsive signature was observed in both Tsk2/+ mice and mice with bleomycin‐induced skin fibrosis. Expression of TNFRSF12A (the TWEAK receptor/fibroblast growth factor–inducible 14) was elevated in skin from patients with fibroproliferative SSc and the skin of Tsk2/+ mice.ConclusionThis study reveals similarities in cutaneous gene expression between distinct mouse models of SSc and specific molecular subsets of the disease. Different pathways underlie the intrinsic subsets including TGFβ, interleukin‐13 (IL‐13), and IL‐4. We identify a novel target, Tnfrsf12a, with elevated expression in skin from patients with fibroproliferative SSc and Tsk2/+ mice. These findings will inform mechanistic and translational preclinical studies in SSc.

Molecular stratification and precision medicine in systemic sclerosis from genomic and proteomic data.

The goal of this review is to summarize recent advances into the pathogenesis and treatment of systemic sclerosis (SSc) from genomic and proteomic studies. Intrinsic gene expression-driven molecular subtypes of SSc are reproducible across three independent datasets. These subsets are a consistent feature of SSc and are found in multiple end-target tissues, such as skin and esophagus. Intrinsic subsets as well as baseline levels of molecular target pathways are potentially predictive of clinical response to specific therapeutics, based on three recent clinical trials. A gene expression-based biomarker of modified Rodnan skin score, a measure of SSc skin severity, can be used as a surrogate outcome metric and has been validated in a recent trial. Proteome analyses have identified novel biomarkers of SSc that correlate with SSc clinical phenotypes. Integrating intrinsic gene expression subset data, baseline molecular pathway information, and serum biomarkers along with surrogate measures of modified Rodnan skin score provides molecular context in SSc clinical trials. With validation, these approaches could be used to match patients with the therapies from which they are most likely to benefit and thus increase the likelihood of clinical improvement.

Gene expression changes reflect clinical response in a placebo-controlled randomized trial of abatacept in patients with diffuse cutaneous systemic sclerosis.

IntroductionSystemic sclerosis is an autoimmune disease characterized by inflammation and fibrosis of the skin and internal organs. We sought to assess the clinical and molecular effects associated with response to intravenous abatacept in patients with diffuse cutaneous systemic.MethodsAdult diffuse cutaneous systemic sclerosis patients were randomized in a 2:1 double-blinded fashion to receive abatacept or placebo over 24 weeks. Primary outcomes were safety and the change in modified Rodnan Skin Score (mRSS) at week 24 compared with baseline. Improvers were defined as patients with a decrease in mRSS of ≥30 % post-treatment compared to baseline. Skin biopsies were obtained for differential gene expression and pathway enrichment analyses and intrinsic gene expression subset assignment.ResultsTen subjects were randomized to abatacept (n = 7) or placebo (n = 3). Disease duration from first non-Raynaud’s symptom was significantly longer (8.8 ± 3.8 years vs. 2.4 ± 1.6 years, p = 0.004) and median mRSS was higher (30 vs. 22, p = 0.05) in the placebo compared to abatacept group. Adverse events were similar in the two groups. Five out of seven patients (71 %) randomized to abatacept and one out of three patients (33 %) randomized to placebo experienced ≥30 % improvement in skin score. Subjects receiving abatacept showed a trend toward improvement in mRSS at week 24 (−8.6 ± 7.5, p = 0.0625) while those in the placebo group did not (−2.3 ± 15, p = 0.75). After adjusting for disease duration, mRSS significantly improved in the abatacept compared with the placebo group (abatacept vs. placebo mRSS decrease estimate −9.8, 95 % confidence interval −16.7 to −3.0, p = 0.0114). In the abatacept group, the patients in the inflammatory intrinsic subset showed a trend toward greater improvement in skin score at 24 weeks compared with the patients in the normal-like intrinsic subset (−13.5 ± 3.1 vs. −4.5 ± 6.4, p = 0.067). Abatacept resulted in decreased CD28 co-stimulatory gene expression in improvers consistent with its mechanism of action. Improvers mapped to the inflammatory intrinsic subset and showed decreased gene expression in inflammatory pathways, while non-improver and placebos showed stable or reverse gene expression over 24 weeks.ConclusionsClinical improvement following abatacept therapy was associated with modulation of inflammatory pathways in skin.Trial registrationClinicalTrials.gov NCT00442611. Registered 1 March 2007.Electronic supplementary materialThe online version of this article (doi:10.1186/s13075-015-0669-3) contains supplementary material, which is available to authorized users.

Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms.

Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6–12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.

Molecular Signatures in Skin Associated with Clinical Improvement during Mycophenolate Treatment in Systemic Sclerosis

Heterogeneity in systemic sclerosis/SSc confounds clinical trials. We previously identified ‘intrinsic’ gene expression subsets by analysis of SSc skin. Here we test the hypotheses that skin gene expression signatures including intrinsic subset are associated with skin score/MRSS improvement during mycophenolate mofetil (MMF) treatment. Gene expression and intrinsic subset assignment were measured in 12 SSc patients’ biopsies and ten controls at baseline, and from serial biopsies of one cyclophosphamide-treated patient, and nine MMF-treated patients. Gene expression changes during treatment were determined using paired t-tests corrected for multiple hypothesis testing. MRSS improved in four of seven MMF-treated patients classified as the inflammatory intrinsic subset. Three patients without MRSS improvement were classified as normal-like or fibroproliferative intrinsic subsets. 321 genes (FDR <5%) were differentially expressed at baseline between patients with and without MRSS improvement during treatment. Expression of 571 genes (FDR <10%) changed between pre- and post-MMF treatment biopsies for patients demonstrating MRSS improvement. Gene expression changes in skin are only seen in patients with MRSS improvement. Baseline gene expression in skin, including intrinsic subset assignment, may identify SSc patients whose MRSS will improve during MMF treatment, suggesting that gene expression in skin may allow targeted treatment in SSc.

Intrinsic Gene Expression Subsets of Diffuse Cutaneous Systemic Sclerosis Are Stable in Serial Skin Biopsies

Skin biopsy gene expression was analyzed by DNA microarray from 13 dSSc patients enrolled in an open label study of rituximab, 9 dSSc patients not treated with rituximab, and 9 healthy controls. These data recapitulate the patient ‘intrinsic’ gene expression subsets described previously including proliferation, inflammatory, and normal-like groups. Serial skin biopsies showed consistent and non-progressing gene expression over time, and importantly, the patients in the inflammatory subset do not move to the fibroproliferative subset, and vice versa. We were unable to detect significant differences in gene expression before and after rituximab treatment, consistent with an apparent lack of clinical response. Serial biopsies from each patient stayed within the same gene expression subset regardless of treatment regimen or the time point at which they were taken. Collectively, these data emphasize the heterogeneous nature of SSc and demonstrate that the intrinsic subsets are an inherent, reproducible and stable feature of the disease that is independent of disease duration. Moreover, these data have fundamental importance for the future development of personalized therapy for SSc; drugs targeting inflammation are likely to benefit those patients with an inflammatory signature, whereas drugs targeting fibrosis are likely to benefit those with a fibroproliferative signature.

Egr-1 Induces a Profibrotic Injury/Repair Gene Program Associated with Systemic Sclerosis

Transforming growth factor-ß (TGF-ß) signaling is implicated in the pathogenesis of fibrosis in scleroderma or systemic sclerosis (SSc), but the precise mechanisms are poorly understood. The immediate-early gene Egr-1 is an inducible transcription factor with key roles in mediating fibrotic TGF-ß responses. To elucidate Egr-1 function in SSc-associated fibrosis, we examined change in gene expression induced by Egr-1 in human fibroblasts at the genome-wide level. Using microarray expression analysis, we derived a fibroblast “Egr-1-responsive gene signature” comprising over 600 genes involved in cell proliferation, TGF-ß signaling, wound healing, extracellular matrix synthesis and vascular development. The experimentally derived “Egr-1-responsive gene signature” was then evaluated in an expression microarray dataset comprising skin biopsies from 27 patients with localized and systemic forms of scleroderma and six healthy controls. We found that the “Egr-1 responsive gene signature” was substantially enriched in the “diffuse-proliferation” subset comprising exclusively of patients with diffuse cutaneous SSc (dcSSc) of skin biopsies. A number of Egr-1-regulated genes was also associated with the “inflammatory” intrinsic subset. Only a minority of Egr-1-regulated genes was concordantly regulated by TGF-ß. These results indicate that Egr-1 induces a distinct profibrotic/wound healing gene expression program in fibroblasts that is associated with skin biopsies from SSc patients with diffuse cutaneous disease. These observations suggest that targeting Egr-1 expression or activity might be a novel therapeutic strategy to control fibrosis in specific SSc subsets.

Comparing job expectations of Chinese male and female college students

PurposeThe purpose of this paper is to examine gender differences in job expectations among Chinese university students and how they may affect the activities of organizations in attracting and retaining talent.Design/methodology/approachSince 1972, a number of studies have utilized a job‐orientation survey instrument developed by Manhardt. This study asked Chinese university students to rate 25 job characteristics according to their importance to the rater on a five‐point scale (5 – very important, 1 – not important) in the same manner as had previously been done by Manhardt and others, subsequently. There were 101 Chinese university business students surveyed.FindingsResults showed that the rank order of importance which male and female students placed on job characteristics was similar. In addition, there were no significant differences between male and female students on either the intrinsic or extrinsic subset of variables, indicating that both genders could be approached similarly with regard to the basic issues that comprise a human resource portfolio.Originality/valueThe consistency of job expectations of males and females could be viewed as a positive characteristic by firms, as it simplifies the human resource portfolio design and thus would be cost effective. Whether such consistency across genders would remain true over time (i.e. as Chinese society and culture evolves) or across all industry segments and geographic regions, or in a variety of economic conditions is an issue for future research exploration and analysis. In regard to certain extrinsic items, females seem to exhibit the philosophy that “life is more than just work” and that flexibility in regard to work design is important. Human resource portfolios would be advised to consider opportunities for cross‐training to better meet such expectations, especially for females.

A TGFβ-Responsive Gene Signature Is Associated with a Subset of Diffuse Scleroderma with Increased Disease Severity

Systemic sclerosis is a complex disease with widespread skin fibrosis and variable visceral organ involvement. Since transforming growth factor-beta (TGFbeta) has been implicated in driving fibrosis in systemic sclerosis, a mechanism-derived gene expression signature was used to assay TGFbeta-responsive gene expression in the skin of patients with systemic sclerosis (SSc). Primary dermal fibroblasts from patients with diffuse SSc (dSSc) and healthy controls were treated with TGFbeta, and the genome-wide gene expression was measured on DNA microarrays over a time course of 24 hours. Eight hundred and ninety-four probes representing 674 uniquely annotated genes were identified as TGFbeta responsive. Expression of the TGFbeta-responsive signature was examined in skin biopsies from 17 dSSc, seven limited SSc (lSSc), three morphea patients, and six healthy controls. The TGFbeta-responsive signature was expressed in 10 out of 17 dSSc skin biopsies, but was not found in lSSc, morphea, or healthy control biopsies. Expression of dSSC the TGFbeta-responsive signature stratifies patients into two major groups, one of which corresponds to the "diffuse-proliferation" intrinsic subset that showed higher modified Rodnan skin score and a higher likelihood of scleroderma lung disease. The TGFbeta-responsive signature is found in only a subset of dSSc patients who could be targeted by specific therapies.