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.

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.

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.

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.

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.

Piwil1 Regulates Glioma Stem Cell Maintenance and Glioblastoma Progression.

Piwi proteins are a subfamily of Argonaute proteins that maintain germ cells in eukaryotes. However, the role of their human homologs in cancer stem cells, and more broadly in cancer, is poorly understood. Here, we report that Piwi-like family members are overexpressed in glioblastoma (GBM), with Piwil1 (Hiwi) most frequently overexpressed (88%). Piwil1 is enriched in glioma stem-like cells (GSCs) to maintain self-renewal. Silencing Piwil1 in GSCs leads to global changes in gene expression resulting in cell-cycle arrest, senescence, or apoptosis. Piwil1 knockdown increases expression of the transcriptional co-regulator BTG2 and the E3-ubiquitin ligase FBXW7, leading to reduced c-Myc expression, as well as loss of expression of stem cell factors Olig2 and Nestin. Piwil1 regulates mRNA stability of BTG2, FBXW7, and CDKN1B. In animal models of GBM, Piwil1 knockdown suppresses tumor growth and promotes mouse survival. These findings support a role of Piwil1 in GSC maintenance and glioblastoma progression.

Publisher Correction: Systemically-delivered biodegradable PLGA alters gut microbiota and induces transcriptomic reprogramming in the liver in an obesity mouse model.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Systemically-delivered biodegradable PLGA alters gut microbiota and induces transcriptomic reprogramming in the liver in an obesity mouse model.

Biodegradable materials, including the widely used poly (lactic-co-glycolic acid) (PLGA) nanoparticles contained in slow-release drug formulations, scaffolds and implants, are ubiquitous in modern biomedicine and are considered inert or capable of being metabolized through intermediates such as lactate. However, in the presence of metabolic stress, such as in obesity, the resulting degradation products may play a detrimental role, which is still not well understood. We evaluated the effect of intravenously-administered PLGA nanoparticles on the gut-liver axis under conditions of caloric excess in C57BL/6 mice. Our results show that PLGA nanoparticles accumulate and cause gut acidification in the cecum, accompanied by significant changes in the microbiome, with a marked decrease of Firmicutes and Bacteroidetes. This was associated with transcriptomic reprogramming in the liver, with a downregulation of mitochondrial function, and an increase in key enzymatic, inflammation and cell activation pathways. No changes were observed in systemic inflammation. Metagenome analysis coupled with publicly available microarray data suggested a mechanism of impaired PLGA degradation and intestinal acidification confirming an important enterohepatic axis of metabolite-microbiome interaction resulting in maintenance of metabolic homeostasis. Thus, our results have important implications for the investigation of PLGA use in metabolically-compromised clinical and experimental settings.

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.

Inflammatory pathway analytes predicting rapid cognitive decline in MCI stage of Alzheimer's disease.

OBJECTIVE: To determine the inflammatory analytes that predict clinical progression and evaluate their performance against biomarkers of neurodegeneration. METHODS: A longitudinal study of MCI-AD patients in a Discovery cohort over 15 months, with replication in the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI cohort over 36 months. Fifty-three inflammatory analytes were measured in the CSF and plasma with a RBM multiplex analyte platform. Inflammatory analytes that predict clinical progression on Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) and Mini Mental State Exam scores were assessed in multivariate regression models. To provide context, key analyte results in ADNI were compared against biomarkers of neurodegeneration, hippocampal volume, and CSF neurofilament light (NfL), in receiver operating characteristic (ROC) analyses evaluating highest quartile of CDR-SB change over two years (≥3 points). RESULTS: Cerebrospinal fluid inflammatory analytes in relation to cognitive decline were best described by gene ontology terms, natural killer cell chemotaxis, and endothelial cell apoptotic process and in plasma, extracellular matrix organization, blood coagulation, and fibrin clot formation described the analytes. CSF CCL2 was most robust in predicting rate of cognitive change and analytes that correlated to CCL2 suggest IL-10 pathway dysregulation. The ROC curves for ≥3 points change in CDR-SB over 2 years when comparing baseline hippocampal volume, CSF NfL, and CCL2 were not significantly different. INTERPRETATION: Baseline levels of immune cell chemotactic cytokine CCL2 in the CSF and IL-10 pathway dysregulation impact longitudinal cognitive and functional decline in MCI-AD. CCL2's utility appears comparable to biomarkers of neurodegeneration in predicting rapid decline.

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.

PARP1 Is Up-Regulated in Non-small Cell Lung Cancer Tissues in the Presence of the Cyanobacterial Toxin Microcystin.

Non-small cell lung cancer (NSCLC) is the major form of lung cancer, with adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being its major subtypes. Smoking alone cannot completely explain the lung cancer etiology. We hypothesize that altered lung microbiome and chronic inflammatory insults in lung tissues contribute to carcinogenesis. Here we explore the microbiome composition of LUAD samples, compared to LUSC and normal samples. Extraction of microbiome DNA in formalin-fixed, paraffin-embedded (FFPE) lung tumor and normal adjacent tissues was meticulously performed. The 16S rRNA product from extracted microbiota was subjected to microbiome amplicon sequencing. To assess the contribution of the host genome, CD36 expression levels were analyzed then integrated with altered NSCLC subtype-specific microbe sequence data. Surprisingly phylum Cyanobacteria was consistently observed in LUAD samples. Across the NSCLC subtypes, differential abundance across four phyla (Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes) was identified based on the univariate analysis (p-value < 6.4e-4 to 3.2e-2). In silico metagenomic and pathway analyses show that presence of microcystin correlates with reduced CD36 and increased PARP1 levels. This was confirmed in microcystin challenged NSCLC (A427) cell lines and Cyanobacteria positive LUAD tissues. Controlling the influx of Cyanobacteria-like particles or microcystin and the inhibition of PARP1 can provide a potential targeted therapy and prevention of inflammation-associated lung carcinogenesis.

Hypoxic Induction of Vasorin Regulates Notch1 Turnover to Maintain Glioma Stem-like Cells.

Tumor hypoxia is associated with poor patient survival and is a characteristic of glioblastoma. Notch signaling is implicated in maintaining glioma stem-like cells (GSCs) within the hypoxic niche, although the molecular mechanisms linking hypoxia to Notch activation have not been clearly delineated. Here we show that Vasorin is a critical link between hypoxia and Notch signaling in GSCs. Vasorin is preferentially induced in GSCs by a HIF1α/STAT3 co-activator complex and stabilizes Notch1 protein at the cell membrane. This interaction prevents Numb from binding Notch1, rescuing it from Numb-mediated lysosomal degradation. Thus, Vasorin acts as a switch to augment Notch signaling under hypoxic conditions. Vasorin promotes tumor growth and reduces survival in mouse models of glioblastoma, and its expression correlates with increased aggression of human gliomas. These findings provide mechanistic insights into how hypoxia promotes Notch signaling in glioma and identify Vasorin as a potential therapeutic target.

Identifying Gene Interaction Networks.

In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. This chapter features two case studies, both of which utilize Cytoscape, an open source and user-friendly network visualization and analysis tool. In the first example, we demonstrate the basic functionalities of Cytoscape by building an interaction network from a publicly available database, analyzing its topological features, and performing gene ontology enrichment. For the second section, we constructed a network from scratch starting with an experimental gene expression dataset. From there, we implement more advanced visual annotations of the network and perform subnetwork enrichment. The methods described are applicable to larger networks that can be collected from various resources.

Mitotic Vulnerability in Triple-Negative Breast Cancer Associated with LIN9 Is Targetable with BET Inhibitors.

Triple-negative breast cancers (TNBC) are highly aggressive, lack FDA-approved targeted therapies, and frequently recur, making the discovery of novel therapeutic targets for this disease imperative. Our previous analysis of the molecular mechanisms of action of bromodomain and extraterminal protein inhibitors (BETi) in TNBC revealed these drugs cause multinucleation, indicating BET proteins are essential for efficient mitosis and cytokinesis. Here, using live cell imaging, we show that BET inhibition prolonged mitotic progression and induced mitotic cell death, both of which are indicative of mitotic catastrophe. Mechanistically, the mitosis regulator LIN9 was a direct target of BET proteins that mediated the effects of BET proteins on mitosis in TNBC. Although BETi have been proposed to function by dismantling super-enhancers (SE), the LIN9 gene lacks an SE but was amplified or overexpressed in the majority of TNBCs. In addition, its mRNA expression predicted poor outcome across breast cancer subtypes. Together, these results provide a mechanism for cancer selectivity of BETi that extends beyond modulation of SE-associated genes and suggest that cancers dependent upon LIN9 overexpression may be particularly vulnerable to BETi. Cancer Res; 77(19); 5395-408. ©2017 AACR.

Microbiomic differences in tumor and paired-normal tissue in head and neck squamous cell carcinomas.

BACKGROUND: While the role of the gut microbiome in inflammation and colorectal cancers has received much recent attention, there are few data to support an association between the oral microbiome and head and neck squamous cell carcinomas. Prior investigations have been limited to comparisons of microbiota obtained from surface swabs of the oral cavity. This study aims to identify microbiomic differences in paired tumor and non-tumor tissue samples in a large group of 121 patients with head and neck squamous cell carcinomas and correlate these differences with clinical-pathologic features. METHODS: Total DNA was extracted from paired normal and tumor resection specimens from 169 patients; 242 samples from 121 patients were included in the final analysis. Microbiomic content of each sample was determined using 16S rDNA amplicon sequencing. Bioinformatic analysis was performed using QIIME algorithms. F-testing on cluster strength, Wilcoxon signed-rank testing on differential relative abundances of paired tumor-normal samples, and Wilcoxon rank-sum testing on the association of T-stage with relative abundances were conducted in R. RESULTS: We observed no significant difference in measures of alpha diversity between tumor and normal tissue (Shannon index: p = 0.13, phylogenetic diversity: p = 0.42). Similarly, although we observed statistically significantly differences in both weighted (p = 0.01) and unweighted (p = 0.04) Unifrac distances between tissue types, the tumor/normal grouping explained only a small proportion of the overall variation in the samples (weighted R2 = 0.01, unweighted R2 < 0.01). Notably, however, when comparing the relative abundances of individual taxa between matched pairs of tumor and normal tissue, we observed that Actinomyces and its parent taxa up to the phylum level were significantly depleted in tumor relative to normal tissue (q < 0.01), while Parvimonas was increased in tumor relative to normal tissue (q = 0.01). These differences were more pronounced among patients with more extensive disease as measured by higher T-stage. CONCLUSIONS: Matched pairs analysis of individual tumor-normal pairs revealed significant differences in relative abundance of specific taxa, namely in the genus Actinomyces. These differences were more pronounced among patients with higher T-stage. Our observations suggest further experiments to interrogate potential novel mechanisms relevant to carcinogenesis associated with alterations of the oral microbiome that may have consequences for the human host.

Network based model of social media big data predicts contagious disease diffusion.

PURPOSE­: Predicting future outbreaks and understanding how they are spreading from location to location can improve patient care provided. Recently, mining social media big data provided the ability to track patterns and trends across the world. This study aims to analyze social media micro-blogs and geographical locations to understand how disease outbreaks spread over geographies and to enhance forecasting of future disease outbreaks. DESIGN/METHODOLOGY/APPROACH ­: In this paper, the authors use Twitter data as the social media data source, influenza-like illnesses (ILI) as disease epidemic and states in the USA as geographical locations. They present a novel network-based model to make predictions about the spread of diseases a week in advance utilizing social media big data. FINDINGS­: The authors showed that flu-related tweets align well with ILI data from the Centers for Disease Control and Prevention (CDC) (p < 0.049). The authors compared this model to earlier approaches that utilized airline traffic, and showed that ILI activity estimates of their model were more accurate. They also found that their disease diffusion model yielded accurate predictions for upcoming ILI activity (p < 0.04), and they predicted the diffusion of flu across states based on geographical surroundings at 76 per cent accuracy. The equations and procedures can be translated to apply to any social media data, other contagious diseases and geographies to mine large data sets. ORIGINALITY/VALUE­: First, while extensive work has been presented utilizing time-series analysis on single geographies, or post-analysis of highly contagious diseases, no previous work has provided a generalized solution to identify how contagious diseases diffuse across geographies, such as states in the USA. Secondly, due to nature of the social media data, various statistical models have been extensively used to address these problems.

FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS.

MOTIVATION: Large scale genomics studies have generated comprehensive molecular characterization of numerous cancer types. Subtypes for many tumor types have been established; however, these classifications are based on molecular characteristics of a small gene sets with limited power to detect dysregulation at the patient level. We hypothesize that frequent graph mining of pathways to gather pathways functionally relevant to tumors can characterize tumor types and provide opportunities for personalized therapies. RESULTS: In this study we present an integrative omics approach to group patients based on their altered pathway characteristics and show prognostic differences within breast cancer (p < 9:57E - 10) and glioblastoma multiforme (p < 0:05) patients. We were able validate this approach in secondary RNA-Seq datasets with p < 0:05 and p < 0:01 respectively. We also performed pathway enrichment analysis to further investigate the biological relevance of dysregulated pathways. We compared our approach with network-based classifier algorithms and showed that our unsupervised approach generates more robust and biologically relevant clustering whereas previous approaches failed to report specific functions for similar patient groups or classify patients into prognostic groups. CONCLUSIONS: These results could serve as a means to improve prognosis for future cancer patients, and to provide opportunities for improved treatment options and personalized interventions. The proposed novel graph mining approach is able to integrate PPI networks with gene expression in a biologically sound approach and cluster patients in to clinically distinct groups. We have utilized breast cancer and glioblastoma multiforme datasets from microarray and RNA-Seq platforms and identified disease mechanisms differentiating samples. SUPPLEMENTARY INFORMATION: Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/dysprog.