Transcriptomic analysis of lung development in wildtype and CFTR-/- sheep suggests an early inflammatory signature in the CF distal lung.

The precise molecular events initiating human lung disease are often poorly characterized. Investigating prenatal events that may underlie lung disease in later life is challenging in man, but insights from the well-characterized sheep model of lung development are valuable. Here, we determine the transcriptomic signature of lung development in wild-type sheep (WT) and use a sheep model of cystic fibrosis (CF) to characterize disease associated changes in gene expression through the pseudoglandular, canalicular, saccular, and alveolar stages of lung growth and differentiation. Using gene ontology process enrichment analysis of differentially expressed genes at each developmental time point, we define changes in biological processes (BP) in proximal and distal lung from WT or CF animals. We also compare divergent BP in WT and CF animals at each time point. Next, we establish the developmental profile of key genes encoding components of ion transport and innate immunity that are pivotal in CF lung disease and validate transcriptomic data by RT-qPCR. Consistent with the known pro-inflammatory phenotype of the CF lung after birth, we observe upregulation of inflammatory response processes in the CF sheep distal lung during the saccular stage of prenatal development. These data suggest early commencement of therapeutic regimens may be beneficial.

The transcriptional repressor BCL11A promotes breast cancer metastasis.

The phenotypes of each breast cancer subtype are defined by their transcriptomes. However, the transcription factors that regulate differential patterns of gene expression that contribute to specific disease outcomes are not well understood. Here, using gene silencing and overexpression approaches, RNA-Seq, and splicing analysis, we report that the transcription factor B-cell leukemia/lymphoma 11A (BCL11A) is highly expressed in triple-negative breast cancer (TNBC) and drives metastatic disease. Moreover, BCL11A promotes cancer cell invasion by suppressing the expression of muscleblind-like splicing regulator 1 (MBNL1), a splicing regulator that suppresses metastasis. This ultimately increases the levels of an alternatively spliced isoform of integrin-α6 (ITGA6), which is associated with worse patient outcomes. These results suggest that BCL11A sustains TNBC cell invasion and metastatic growth by repressing MBNL1-directed splicing of ITGA6 Our findings also indicate that BCL11A lies at the interface of transcription and splicing and promotes aggressive TNBC phenotypes.

Evaluation of Microbiome Alterations Following Consumption of BIOHM, a Novel Probiotic.

Gastrointestinal microbiome dysbiosis may result in harmful effects on the host, including those caused by inflammatory bowel diseases (IBD). The novel probiotic BIOHM, consisting of Bifidobacterium breve, Saccharomyces boulardii, Lactobacillus acidophilus, L. rhamnosus, and amylase, was developed to rebalance the bacterial-fungal gut microbiome, with the goal of reducing inflammation and maintaining a healthy gut population. To test the effect of BIOHM on human subjects, we enrolled a cohort of 49 volunteers in collaboration with the Fermentation Festival group (Santa Barbara, CA, USA). The profiles of gut bacterial and fungal communities were assessed via stool samples collected at baseline and following 4 weeks of once-a-day BIOHM consumption. Mycobiome analysis following probiotic consumption revealed an increase in Ascomycota levels in enrolled individuals and a reduction in Zygomycota levels (p value < 0.01). No statistically significant difference in Basidiomycota was detected between pre- and post-BIOHM samples and control abundance profiles (p > 0.05). BIOHM consumption led to a significant reduction in the abundance of Candida genus in tested subjects (p value < 0.013), while the abundance of C. albicans also trended lower than before BIOHM use, albeit not reaching statistical significance. A reduction in the abundance of Firmicutes at the phylum level was observed following BIOHM use, which approached levels reported for control individuals reported in the Human Microbiome Project data. The preliminary results from this clinical study suggest that BIOHM is capable of significantly rebalancing the bacteriome and mycobiome in the gut of healthy individuals, suggesting that further trials examining the utility of the BIOHM probiotic in individuals with gastrointestinal symptoms, where dysbiosis is considered a source driving pathogenesis, are warranted.

A functional genomics approach to investigate the differentiation of iPSCs into lung epithelium at air-liquid interface.

The availability of robust protocols to differentiate induced pluripotent stem cells (iPSCs) into many human cell lineages has transformed research into the origins of human disease. The efficacy of differentiating iPSCs into specific cellular models is influenced by many factors including both intrinsic and extrinsic features. Among the most challenging models is the generation of human bronchial epithelium at air-liquid interface (HBE-ALI), which is the gold standard for many studies of respiratory diseases including cystic fibrosis. Here, we perform open chromatin mapping by ATAC-seq and transcriptomics by RNA-seq in parallel, to define the functional genomics of key stages of the iPSC to HBE-ALI differentiation. Within open chromatin peaks, the overrepresented motifs include the architectural protein CTCF at all stages, while motifs for the FOXA pioneer and GATA factor families are seen more often at early stages, and those regulating key airway epithelial functions, such as EHF, are limited to later stages. The RNA-seq data illustrate dynamic pathways during the iPSC to HBE-ALI differentiation, and also the marked functional divergence of different iPSC lines at the ALI stages of differentiation. Moreover, a comparison of iPSC-derived and lung donor-derived HBE-ALI cultures reveals substantial differences between these models.

Proteome Dynamics Reveals Pro-Inflammatory Remodeling of Plasma Proteome in a Mouse Model of NAFLD.

Nonalcoholic fatty liver disease (NAFLD) is associated with an increased risk of cardiovascular disease. Because the liver is the major source of circulatory proteins, it is not surprising that hepatic disease could lead to alterations in the plasma proteome, which are therein implicated in atherosclerosis. The current study used low-density lipoprotein receptor-deficient (LDLR(-/-)) mice to examine the impact of Western diet (WD)-induced NAFLD on plasma proteome homeostasis. Using a (2)H2O-metabolic labeling method, we found that a WD led to a proinflammatory distribution of circulatory proteins analyzed in apoB-depleted plasma, which was attributed to an increased production. The fractional turnover rates of short-lived proteins that are implicated in stress-response, lipid metabolism, and transport functions were significantly increased with WD (P < 0.05). Pathway analyses revealed that alterations in plasma proteome dynamics were related to the suppression of hepatic PPARα, which was confirmed based on reduced gene and protein expression of PPARα in mice fed a WD. These changes were associated with ∼4-fold increase (P < 0.0001) in the proinflammatory property of apoB-depleted plasma. In conclusion, the proteome dynamics method reveals proinflammatory remodeling of the plasma proteome relevant to liver disease. The approach used herein may provide a useful metric of in vivo liver function and better enable studies of novel therapies surrounding NAFLD and other diseases.

Proteomic and bioinformatics profile of paired human alveolar macrophages and peripheral blood monocytes.

Little is known about proteomic differences between pluripotent human peripheral blood monocytes (MN) and their terminally-differentiated pulmonary counterparts, alveolar macrophages (AM). To better characterize these cell populations, we performed a label-free shotgun proteomics assessment of matched AM and MN preparations from eight healthy volunteers. With an FDR of less than 0.45%, we identified 1754 proteins within AM and 1445 from MN. Comparison of the two proteomes revealed that 1239 of the proteins found in AM were shared with MN, whereas 206 proteins were uniquely identified in MN and 515 were unique to AM. Molecular and cellular functions, protein classes, development associations, and membership in physiological systems and canonical pathways were identified among the detected proteins. Analysis of biologic processes represented by these proteomes indicated that MN were most prominently enriched for proteins involved in cellular movement and immune cell trafficking. In contrast, AM were enriched for proteins involved in protein trafficking, molecular transport, and cellular assembly and organization. These findings provide a baseline proteomic resource for further studies aimed at better understanding of the functional differences between MN and AM in both health and disease.

Microbiomic subprofiles and MDR1 promoter methylation in head and neck squamous cell carcinoma.

Clinical observations and epidemiologic studies suggest that the incidence of head and neck squamous cell carcinoma (HNSCC) correlates with dental hygiene, implying a role for bacteria-induced inflammation in its pathogenesis. Here we begin to explore the pilot hypothesis that specific microbial populations may contribute to HNSCC pathogenesis via epigenetic modifications in inflammatory- and HNSCC-associated genes. Microbiomic profiling by 16S rRNA sequencing of matched tumor and adjacent normal tissue specimens in 42 individuals with HNSCC demonstrate a significant association of specific bacterial subpopulations with HNSCC over normal tissue (P < 0.01). Furthermore, microbial populations can separate tumors by tobacco status (P < 0.008), but not by alcohol status (P = 0.41). If our subhypothesis regarding a mechanistic link from microorganism to carcinogenesis via inflammation and consequent aberrant DNA methylation is correct, then we should see hypermethylation of relevant genes associate with specific microbiomic profiles. Methylation analysis in four genes (MDR1, IL8, RARB, TGFBR2) previously linked to HNSCC or inflammation shows significantly increased methylation in tumor samples compared with normal oral mucosa. Of these, MDR1 promoter methylation associates with specific microbiomic profiles in tumor over normal mucosa. Additionally, we report that MDR1 methylation correlates with regional nodal metastases in the context of two specific bacterial subpopulations, Enterobacteriaceae and Tenericutes (P < 0.001 for each). These associations may lead to a different, and potentially more comprehensive, perspective on the pathogenesis of HNSCC, and support further exploration of mechanistic linkage and, if so, novel therapeutic strategies such as demethylating agents and probiotic adjuncts, particularly for patients with advanced or refractory disease.

Hypomethylation of the MMP7 promoter and increased expression of MMP7 distinguishes the basal-like breast cancer subtype from other triple-negative tumors.

Identification of novel targets for the treatment of basal-like breast cancer is essential for improved outcomes in patients with this disease. This study investigates the association of MMP7 expression and MMP7 promoter methylation with subtype and outcome in breast cancer patient cohorts. Immunohistochemical analysis was performed on a breast cancer tissue microarray and validated in independent histological samples. MMP7 expression significantly correlated with patient age, tumor size, triple-negative (TN) status, and recurrence. Analysis of publically available datasets confirmed MMP7 gene expression as a prognostic marker of breast cancer metastasis, particularly metastasis to the brain and lungs. Methylation of the MMP7 promoter was assessed by methylation-specific PCR in a panel of breast cancer cell lines and patient tumor samples. Hypomethylation of the MMP7 promoter significantly correlated with TN status in DNA from patient tumor samples, and this association was confirmed using The Cancer Genome Atlas (TCGA) dataset. Evaluation of a panel of breast cancer cell lines and data from the Curtis and TCGA breast carcinoma datasets revealed that elevated MMP7 expression and MMP7 promoter hypomethylation are specific biomarkers of the basal-like molecular subtype which shares considerable, but not complete, overlap with the clinical TN subtype. Importantly, MMP7 expression was identified as an independent predictor of pathological complete response in a large breast cancer patient cohort. Combined, these data suggest that MMP7 expression and MMP7 promoter methylation may be useful as prognostic biomarkers. Furthermore, MMP7 expression and promoter methylation analysis may be effective mechanisms to distinguish basal-like breast cancers from other triple-negative subtypes. Finally, these data implicate MMP7 as a potential therapeutic target for the treatment of basal-like breast cancers.

Network biology methods integrating biological data for translational science.

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

Proteomic analysis of synovial fluid from the osteoarthritic knee: comparison with transcriptome analyses of joint tissues.

OBJECTIVE: The pathophysiology of the most common joint disease, osteoarthritis (OA), remains poorly understood. Since synovial fluid (SF) bathes joint cartilage and synovium, we reasoned that a comparative analysis of its protein constituents in health and OA could identify pathways involved in joint damage. We undertook this study to perform a proteomic analysis of knee SF from OA patients and control subjects and to compare the results to microarray expression data from cartilage and synovium. METHODS: Age-matched knee SF samples from 10 control subjects, 10 patients with early-stage OA, and 10 patients with late-stage OA were compared using 2-dimensional difference-in-gel electrophoresis and mass spectrometry (MS). MS with a multiplexed peptide selected reaction monitoring assay was used to confirm differential expression of a subset of proteins in an independent OA patient cohort. Proteomic results were analyzed by Ingenuity Pathways Analysis and compared to published synovial tissue and cartilage messenger RNA profiles. RESULTS: Sixty-six proteins were differentially present in healthy and OA SF. Three major pathways were identified among these proteins: the acute-phase response signaling pathway, the complement pathway, and the coagulation pathway. Differential expression of 5 proteins was confirmed by selected reaction monitoring assay. A focused analysis of transcripts corresponding to the differentially present proteins indicated that both synovial and cartilage tissues may contribute to the OA SF proteome. CONCLUSION: Proteins involved in the acute-phase response signaling pathway, the complement pathway, and the coagulation pathway are differentially regulated in SF from OA patients, suggesting that they contribute to joint damage. Validation of these pathways and their utility as biomarkers or therapeutic targets in OA is warranted.

MAGNET: MicroArray Gene expression and Network Evaluation Toolkit.

MicroArray Gene expression and Network Evaluation Toolkit (MAGNET) is a web-based application that provides tools to generate and score both protein-protein interaction networks and coexpression networks. MAGNET integrates user-provided experimental measurements with high-throughput proteomic datasets, generating weighted gene-gene and protein-protein interaction networks. MAGNET allows users to weight edges of protein-protein interaction networks using a logistic regression model integrating tissue-specific gene expression data, sub-cellular localization data, co-clustering of interacting proteins and the number of observations of the interaction. This provides a way to quantitatively measure the plausibility of interactions in protein-protein interaction networks given protein/gene expression measurements. Secondly, MAGNET generates filtered coexpression networks, where genes are represented as nodes, and their correlations are represented with edges. Overall, MAGNET provides researchers with a new framework with which to analyze and generate gene-gene and protein-protein interaction networks, based on both the user's own data and publicly available -omics datasets. The freely available service and documentation can be accessed at http://gurkan.case.edu/software or http://magnet.case.edu.

nSEA: n-Node Subnetwork Enumeration Algorithm Identifies Lower Grade Glioma Subtypes with Altered Subnetworks and Distinct Prognostics.

Advances in molecular characterization have reshaped our understanding of low-grade glioma (LGG) subtypes, emphasizing the need for comprehensive classification beyond histology. Lever-aging this, we present a novel approach, network-based Subnetwork Enumeration, and Analysis (nSEA), to identify distinct LGG patient groups based on dysregulated molecular pathways. Using gene expression profiles from 516 patients and a protein-protein interaction network we generated 25 million sub-networks. Through our unsupervised bottom-up approach, we selected 92 subnetworks that categorized LGG patients into five groups. Notably, a new LGG patient group with a lack of mutations in EGFR, NF1, and PTEN emerged as a previously unidentified patient subgroup with unique clinical features and subnetwork states. Validation of the patient groups on an independent dataset demonstrated the robustness of our approach and revealed consistent survival traits across different patient populations. This study offers a comprehensive molecular classification of LGG, providing insights beyond traditional genetic markers. By integrating network analysis with patient clustering, we unveil a previously overlooked patient subgroup with potential implications for prognosis and treatment strategies. Our approach sheds light on the synergistic nature of driver genes and highlights the biological relevance of the identified subnetworks. With broad implications for glioma research, our findings pave the way for further investigations into the mechanistic underpinnings of LGG subtypes and their clinical relevance.Availability: Source code and supplementary data are available at https://github.com/bebeklab/nSEA.

A minimally Invasive Biomarker for Sensitive and Accurate Diagnosis of Parkinson's Disease.

Importance: Parkinson's disease (PD), the second most common neurodegenerative disease, is pathologically characterized by intraneuronal deposition of misfolded alpha-synuclein aggregates (αSyn D ). αSyn D seeding activities in CSF and skin samples have shown great promise in PD diagnosis, but they require invasive procedures. Sensitive and accurate αSyn D seed amplification assay (αSyn-SAA) for more accessible and minimally invasive samples (such as blood and saliva) are urgently needed for PD pathological diagnosis in routine clinical practice. Objective: To develop a sensitive and accurate αSyn-SAA biomarker using blood and saliva samples for sensitive, accurate and minimally invasive PD diagnosis. Design Setting and Participants: This prospective diagnostic study evaluates serum and saliva samples collected from patients clinically diagnosed with PD or healthy controls (HC) without PD at an academic Parkinson's and Movement Disorders Center from February 2020 to March 2024. Patients diagnosed with non-PD parkinsonism were excluded from this analysis. A total of 124 serum samples (82 PD and 42 HC) and 131 saliva samples (83 PD and 48 HC) were collected and examined by αSyn-SAA. Out of the 124 serum donors, a subset of 74 subjects (48 PD and 26 HC) also donated saliva samples during the same visits. PD patients with serum samples had a mean age of 69.21 years (range 44-88); HC subjects with serum samples had a mean age of 66.55 years (range 44-81); PD patients with saliva samples had a mean age of 69.58 years (range 49-87); HC subjects with saliva samples had a mean age of 64.71 years (range 30-81). Main Outcomes and Measures: Serum and/or saliva αSyn D seeding activities from PD and HC subjects were measured by αSyn-SAA using the Real-Time Quaking-Induced Conversion (RT-QuIC) platform. These PD patients had extensive clinical assessments including MDS-UPDRS. For a subset of PD and HC subjects whose serum and saliva samples were both collected during the same visits, the αSyn D seeding activities in both samples from the same subjects were examined, and the diagnostic accuracies for PD based on the seeding activities in either sample alone or both samples together were compared. Results: RT-QuIC analysis of αSyn D seeding activities in the 124 serum samples revealed a sensitivity of 80.49%, a specificity of 90.48%, and an accuracy of 0.9006 (AUC of ROC, 95% CI, 0.8472-0.9539, p <0.0001) for PD diagnosis. RT-QuIC analysis of αSyn D seeding activity in 131 saliva samples revealed a sensitivity of 74.70%, a specificity of 97.92%, and an accuracy of 0.8966 (AUC of ROC, 95% CI, 0.8454-0.9478, p <0.0001). When aSyn D seeding activities in the paired serum-saliva samples from the subset of 48 PD and 26 HC subjects were considered together, sensitivity was 95.83%, specificity was 96.15%, and the accuracy was 0.98 (AUC of ROC, 95% CI, 0.96-1.00, p <0.001), which are significantly better than when αSyn D seeding activities in either serum or saliva were used alone. For the paired serum-saliva samples, when specificity was set at 100% by elevating the αSyn-SAA cutoff values, a sensitivity of 91.7% and an accuracy of 0.9457 were still attained. Detailed correlation analysis revealed that αSyn D seeding activities in the serum of PD patients were correlated inversely with Montreal Cognitive Assessment (MoCA) score ( p =0.04), positively with Hamilton Depression Rating Scale (HAM-D) ( p =0.03), and weakly positively with PDQ-39 cognitive impairment score ( p =0.07). Subgroup analysis revealed that the inverse correlation with MoCA was only seen in males ( p =0.013) and weakly in the ≥70 age group ( p =0.07), and that the positive correlation with HAM-D was only seen in females ( p =0.04) and in the <70 age group ( p =0.01). In contrast, αSyn D seeding activities in the saliva of PD patients were inversely correlated with age at diagnosis ( p =0.02) and the REM sleep behavior disorder (RBD) status ( p =0.04), but subgroup analysis showed that the inverse correlation with age at diagnosis was only seen in males ( p =0.04) and in the <70 age group ( p =0.01). Conclusion and Relevance: Our data show that concurrent RT-QuIC assay of αSyn D seeding activities in both serum and saliva can achieve high diagnostic accuracies comparable to that of CSF αSyn-SAA, suggesting that αSyn D seeding activities in serum and saliva together can potentially be used as a valuable biomarker for highly sensitive, accurate, and minimally invasive diagnosis of PD in routine clinical practice. αSyn D seeding activities in serum and saliva of PD patients correlate differentially with some clinical characteristics and in an age and sex-dependent manner. KEY POINTS: Question: Are αSyn D seeding activities in serum and saliva together a more sensitive and accurate diagnostic PD biomarker than αSyn D seeding activities in either sample type alone? Are αSyn D seeding activities in either serum or saliva correlated with any clinical characteristics? Findings: Examinations of αSyn D seeding activities in 124 serum samples and 131 saliva samples from PD and heathy control subjects show that αSyn D seeding activities in both serum and saliva samples together can provide significantly more sensitive and accurate diagnosis of PD than either sample type alone. αSyn D seeding activities in serum or saliva exhibit varied inverse or positive correlations with some clinical features in an age and sex-dependent manner. Meaning: αSyn D seeding activities in serum and saliva together can potentially be used as a valuable pathological biomarker for highly sensitive, accurate, and minimally invasive PD diagnosis in routine clinical practice and clinical studies, and αSyn D seeding activities in serum or saliva correlate with some clinical characteristics in an age and sex-dependent manner, suggesting some possible clinical utility of quantitative serum/saliva αSyn-SAA data.

Human alveolar macrophages display marked hypo-responsiveness to IFN-γ in both proteomic and gene expression analysis.

Alveolar macrophages (AM) perform a primary defense mechanism in the lung through phagocytosis of inhaled particles and microorganisms. AM are known to be relatively immunosuppressive consistent with the aim to limit alveolar inflammation and maintain effective gas exchange in the face of these constant challenges. How AM respond to T cell derived cytokine signals, which are critical to the defense against inhaled pathogens, is less well understood. For example, successful containment of Mycobacterium tuberculosis (Mtb) in lung macrophages is highly dependent on IFN-γ secreted by Th-1 lymphocytes, however, the proteomic IFN-γ response profile in AM remains mostly unknown. In this study, we measured IFN-γ induced protein abundance changes in human AM and autologous blood monocytes (MN). AM cells were activated by IFN-γ stimulation resulting in STAT1 phosphorylation and production of MIG/CXCL9 chemokine. However, the global proteomic response to IFN-γ in AM was dramatically limited in comparison to that of MN (9 AM vs 89 MN differentially abundant proteins). AM hypo-responsiveness was not explained by reduced JAK-STAT1 signaling nor increased SOCS1 expression. These findings suggest that AM have a tightly regulated response to IFN-γ which may prevent excessive pulmonary inflammation but may also provide a niche for the initial survival and growth of Mtb and other intracellular pathogens in the lung.

Proteomic and bioinformatic profile of primary human oral epithelial cells.

Wounding of the oral mucosa occurs frequently in a highly septic environment. Remarkably, these wounds heal quickly and the oral cavity, for the most part, remains healthy. Deciphering the normal human oral epithelial cell (NHOEC) proteome is critical for understanding the mechanism(s) of protection elicited when the mucosal barrier is intact, as well as when it is breached. Combining 2D gel electrophoresis with shotgun proteomics resulted in identification of 1662 NHOEC proteins. Proteome annotations were performed based on protein classes, molecular functions, disease association and membership in canonical and metabolic signaling pathways. Comparing the NHOEC proteome with a database of innate immunity-relevant interactions (InnateDB) identified 64 common proteins associated with innate immunity. Comparison with published salivary proteomes revealed that 738/1662 NHOEC proteins were common, suggesting that significant numbers of salivary proteins are of epithelial origin. Gene ontology analysis showed similarities in the distributions of NHOEC and saliva proteomes with regard to biological processes, and molecular functions. We also assessed the interindividual variability of the NHOEC proteome and observed it to be comparable with other primary cells. The baseline proteome described in this study should serve as a resource for proteome studies of the oral mucosa, especially in relation to disease processes.

Specific kinesin expression profiles associated with taxane resistance in basal-like breast cancer.

Breast cancer is a genetically heterogenous disease with subtypes differing in prognosis and chemosensitivity. The basal-like breast cancer (BLBC) molecular subtype is associated with poorer outcomes, but is more responsive to taxane-based chemotherapy. Kinesins are intracellular transport proteins that interact with microtubules, which are also the mechanistic target for taxanes. We investigated the relationship between taxane resistance in BLBC and kinesins using both expression and functional studies. Kinesin (KIF) expression was evaluated in three settings in relation to taxane resistance: (i) the NCI-60 cancer cell lines, (ii) pre-treatment samples from four BLBC patient cohorts receiving neoadjuvant chemotherapy regimens with and without taxanes, and (iii) post-treatment samples from residual breast cancer following neoadjuvant taxane-containing chemotherapy. We used a novel functional approach to gene modification, validation-based insertional mutagenesis, to select kinesin-overexpressing clones of BLBC cells for evaluation of related mechanisms of taxane resistance. In the NCI-60 cell line dataset, overexpression of the kinesin KIFC3 is significantly correlated with resistance to both docetaxel (P < 0.001) and paclitaxel (P < 0.001), but not to platinum-based chemotherapy, including carboplatin (P = 0.49) and cisplatin (P = 0.10). Overexpression of KIFC3 and KIF5A in pre-chemotherapy samples similarly predicted resistance to paclitaxel in the MDACC cohorts (P = 0.01); no KIF predicted resistance to fluorouracil-epirubicin-cyclophosphamide or cisplatin in BLBC patient cohorts treated without taxanes. KIF12 is the most overexpressed KIF gene in post-chemotherapy taxane-resistant residual breast cancers (2.8-fold-change). Functional studies established that overexpression of KIFC3, KIF5A, and KIF12 were specific in mediating resistance to docetaxel and not vincristine or doxorubicin. Mutation of the ATP-binding domain of a kinesin abolished its ability to mediate docetaxel resistance. Overall, kinesin overexpression correlates with specific taxane resistance in BLBC cell lines and tissues. Our results suggest a novel approach for drug development to overcome taxane resistance in breast cancer through concurrent or sequential use of kinesin inhibitors.

BiC: a web server for calculating bimodality of coexpression between gene and protein networks.

UNLABELLED: Bimodal patterns of expression have recently been shown to be useful not only in prioritizing genes that distinguish phenotypes, but also in prioritizing network models that correlate with proteomic evidence. In particular, subgroups of strongly coexpressed gene pairs result in an increased variance of the correlation distribution. This variance, a measure of association between sets of genes (or proteins), can be summarized as the bimodality of coexpression (BiC). We developed an online tool to calculate the BiC for user-defined gene lists and associated mRNA expression data. BiC is a comprehensive application that provides researchers with the ability to analyze both publicly available and user-collected array data. AVAILABILITY: The freely available web service and the documentation can be accessed at http://gurkan.case.edu/software. CONTACT: gurkan@case.edu.

Germline mutations in MSR1, ASCC1, and CTHRC1 in patients with Barrett esophagus and esophageal adenocarcinoma.

CONTEXT: Barrett esophagus (BE) occurs in 1% to 10% of the general population and is believed to be the precursor of esophageal adenocarcinoma (EAC). The incidence of EAC has increased 350% in the last 3 decades without clear etiology. Finding predisposition genes may improve premorbid risk assessment, genetic counseling, and management. Genome-wide multiplatform approaches may lead to the identification of genes important in BE/EAC development. OBJECTIVE: To identify risk alleles or mutated genes associated with BE/EAC. DESIGN, SETTING, AND PATIENTS: Model-free linkage analyses of 21 concordant-affected sibling pairs with BE/EAC and 11 discordant sibling pairs (2005-2006). Significant germline genomic regions in independent prospectively accrued series of 176 white patients with BE/EAC and 200 ancestry-matched controls (2007-2010) were validated and fine mapped. Integrating data from these significant genomic regions with somatic gene expression data from 19 BE/EAC tissues yielded 12 "priority" candidate genes for mutation analysis (2010). Genes that showed mutations in cases but not in controls were further screened in an independent prospectively accrued validation series of 58 cases (2010). MAIN OUTCOME MEASURES: Identification of germline mutations in genes associated with BE/EAC cases. Functional interrogation of the most commonly mutated gene. RESULTS: Three major genes, MSR1, ASCC1, and CTHRC1 were associated with BE/EAC (all P < .001). In addition, 13 patients (11.2%) with BE/EAC carried germline mutations in MSR1, ASCC1, or CTHRC1. MSR1 was the most frequently mutated, with 8 of 116 (proportion, 0.069; 95% confidence interval [CI], 0.030-0.130; P < .001) cases with c.877C>T (p.R293X). An independent validation series confirmed germline MSR1 mutations in 2 of 58 cases (proportion, 0.035; 95% CI, 0.004-0.120; P = .09). MSR1 mutation resulted in CCND1 up-regulation in peripheral-protein lysate. Immunohistochemistry of BE tissues in MSR1-mutation carriers showed increased nuclear expression of CCND1. CONCLUSION: MSR1 was significantly associated with the presence of BE/EAC in derivation and validation samples, although it was only present in a small percentage of the cases.

Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers.

Molecular mechanisms characterizing cancer development and progression are complex and process through thousands of interacting elements in the cell. Understanding the underlying structure of interactions requires the integration of cellular networks with extensive combinations of dysregulation patterns. Recent pan-cancer studies focused on identifying common dysregulation patterns in a confined set of pathways or targeting a manually curated set of genes. However, the complex nature of the disease presents a challenge for finding pathways that would constitute a basis for tumor progression and requires evaluation of subnetworks with functional interactions. Uncovering these relationships is critical for translational medicine and the identification of future therapeutics. We present a frequent subgraph mining algorithm to find functional dysregulation patterns across the cancer spectrum. We mined frequent subgraphs coupled with biased random walks utilizing genomic alterations, gene expression profiles, and protein-protein interaction networks. In this unsupervised approach, we have recovered expert-curated pathways previously reported for explaining the underlying biology of cancer progression in multiple cancer types. Furthermore, we have clustered the genes identified in the frequent subgraphs into highly connected networks using a greedy approach and evaluated biological significance through pathway enrichment analysis. Gene clusters further elaborated on the inherent heterogeneity of cancer samples by both suggesting specific mechanisms for cancer type and common dysregulation patterns across different cancer types. Survival analysis of sample level clusters also revealed significant differences among cancer types (p < 0.001). These results could extend the current understanding of disease etiology by identifying biologically relevant interactions.Supplementary Information: Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/FSM_Pancancer.

TNFRSF1B Gene Variants and Related Soluble TNFR2 Levels Impact Resilience in Alzheimer's Disease.

Tumor necrosis factor receptor 2 (TNFR2) promotes neuronal survival downstream. This longitudinal study evaluated whether the TNFRSF1B gene encoding TNFR2 and levels of its soluble form (sTNFR2) affect Alzheimer disease (AD) biomarkers and clinical outcomes. Data analyzed included 188 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had mild cognitive impairment (MCI) and AD dementia. Further, a replication study was performed in 48 patients with MCI with positive AD biomarkers who were treated at a memory clinic. Cerebrospinal fluid (CSF) sTNFR2 levels along with two related TNFRSF1B gene single nucleotide polymorphisms (SNPs) rs976881 and rs1061622 were assessed. General linear models were used to evaluate the effect of CSF sTNFR2 levels and each SNP in relationship to CSF t-tau and p-tau, cognitive domains, MRI brain measures, and longitudinal cognitive changes after adjustments were made for covariates such as APOE ε4 status. In the ADNI cohort, a significant interaction between rs976881 and CSF sTNFR2 modulates CSF t-tau and p-tau levels; hippocampal and whole brain volumes; and Digit Span Forwards subtest scores. In the replication cohort, a significant interaction between rs976881 and CSF sTNFR2 modulates CSF p-tau. A significant interaction between rs976881 and CSF sTNFR2 also impacts Clinical Dementia Rating Sum of Boxes scores over 12 months in the ADNI cohort. The interaction between TNFRSF1B variant rs976881 and CSF sTNFR2 levels was noted to modulate multiple AD-associated severity markers and cognitive domains. This interaction impacts resilience-related clinical outcomes in AD and lends support to sTNFR2 as a promising candidate for therapeutic targeting to improve clinical outcomes of interest.