Trillions of microbes inhabit the human intestine, forming a complex ecological community that influences normal physiology and susceptibility to disease through its collective metabolic activities and host interactions. Understanding the factors that underlie changes in the composition and function of the gut microbiota will aid in the design of therapies that target it. This goal is formidable. The gut microbiota is immensely diverse, varies between individuals and can fluctuate over time — especially during disease and early development. Viewing the microbiota from an ecological perspective could provide insight into how to promote health by targeting this microbial community in clinical treatments.

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Diversity, stability and resilience of the human gut microbiota

We are facing a global metabolic health crisis provoked by an obesity epidemic. Here we report the human gut microbial composition in a population sample of 123 non-obese and 169 obese Danish individuals. We find two groups of individuals that differ by the number of gut microbial genes and thus gut bacterial richness. They contain known and previously unknown bacterial species at different proportions; individuals with a low bacterial richness (23% of the population) are characterized by more marked overall adiposity, insulin resistance and dyslipidaemia and a more pronounced inflammatory phenotype when compared with high bacterial richness individuals. The obese individuals among the lower bacterial richness group also gain more weight over time. Only a few bacterial species are sufficient to distinguish between individuals with high and low bacterial richness, and even between lean and obese participants. Our classifications based on variation in the gut microbiome identify subsets of individuals in the general white adult population who may be at increased risk of progressing to adiposity-associated co-morbidities.

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Richness of human gut microbiome correlates with metabolic markers

AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore

PL02-05 

All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.

Patterns of Somatic Mutation in Human Cancer Genomes

The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony (
 https://github.com/immunogenomics/harmony
 ), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data. Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.

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Fast, sensitive and accurate integration of single-cell data with Harmony.

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature-selection algorithm that addresses several major issues with prior work, including problems with algorithm implementation, computational complexity, and solution accuracy. The key idea is to decompose an arbitrarily complex nonlinear problem into a set of locally linear ones through local learning, and then learn feature relevance globally within the large margin framework. The proposed algorithm is based on well-established machine learning and numerical analysis techniques, without making any assumptions about the underlying data distribution. It is capable of processing many thousands of features within minutes on a personal computer while maintaining a very high accuracy that is nearly insensitive to a growing number of irrelevant features. Theoretical analyses of the algorithm's sample complexity suggest that the algorithm has a logarithmical sample complexity with respect to the number of features. Experiments on 11 synthetic and real-world data sets demonstrate the viability of our formulation of the feature-selection problem for supervised learning and the effectiveness of our algorithm.

/pdf/local-learning-based-feature-selection-for-high-dimensional-34ijnepwnp.pdf

Local-Learning-Based Feature Selection for High-Dimensional Data Analysis

Bladder cancer is one of the most prevalent cancers worldwide, but the treatment and management of this disease can be very successful if the disease is detected early The development of molecular assays that could diagnose bladder cancer accurately, and at an early stage, would be a significant advance Ideally, such molecular assays would be applicable to non-invasively obtained body fluids, and be designed not only for diagnosis but also for monitoring disease recurrence and response to treatment In this article, we assess the performance of current diagnostic assays for bladder cancer and discuss some of the emerging biomarkers that could be developed to augment current bladder cancer detection strategies

/pdf/bladder-cancer-detection-and-monitoring-assessment-of-urine-1w7n66xaur.pdf

Bladder Cancer Detection and Monitoring: Assessment of Urine- and Blood-Based Marker Tests

The ends of linear chromosomes are capped by specialized nucleoprotein structures termed telomeres. Telomeres comprise tracts of noncoding hexanucleotide repeat sequences that, in combination with specific proteins, protect against degradation, rearrangement, and chromosomal fusion events. Due to the polarity of conventional DNA synthesis, a net loss of telomeric sequences occurs at each cell division. It has been proposed that this cumulative telomeric erosion is a limiting factor in replicative capacity and elicits a signal for the onset of cellular senescence. To proliferate beyond the senescent checkpoint, cells must restore telomere length. This can be achieved by telomerase, an enzyme with reverse-transcriptase activity. This enzyme is absent in differentiated somatic tissues, but telomerase reactivation has been detected in most tumors. Much investigative effort is focusing on telomere dynamics with a view to possible manipulation of cellular proliferative potential. In this article, we review the role of telomeres and telomerase in senescence and tumor progression, and we discuss the potential use of telomerase in diagnosis and treatment.

Role of telomerase in cell senescence and oncogenesis

Expression of CXCL1 in human endothelial cells induces angiogenesis through the CXCR2 receptor and the ERK1/2 and EGF pathways.

This study investigated CD44 gene expression at both the RNA and protein level in well differentiated superficial and in deeply invasive bladder carcinomas. Proteins were studied by immunohistochemistry using antibodies against standard (CD44s) and variant (CD44v) isoforms. mRNA was analyzed by reverse transcriptase polymerase chain reaction/Southern blotting and in situ hybridization. Immunostaining with antibodies against CD44s and CD44v2, -5 and -6 (exons 7, 10, and 11, respectively) showed that carcinoma cells in all papillary tumors expressed strong signals throughout the epithelium but especially in the basal layer, which abuts on the stroma. However, invasive tumors, which are believed to originate mainly from flat urothelial tumors or, less frequently, from papillary carcinomas, progressively lost CD44 proteins as they penetrated deeper and became less differentiated. This change was paralleled at the mRNA level, by the gradual loss of expression of CD44s and CD44v transcripts in deeply invasive tumors until they were virtually undetectable. Conversely, papillary tumors contained multiple higher molecular weight transcripts, suggesting that the loss of CD44 proteins in the more aggressive tumors is due to a disturbance in transcription. This concept was confirmed by in situ hybridization studies with a probe showing that substantial variant CD44 mRNA is located in the urothelium in papillary carcinomas but is absent from deeply invasive carcinomas. These results indicate that initially there is a striking increase in CD44 gene expression in early bladder carcinomas, relative to normal urothelium, but that this diminishes as the tumors acquire a more aggressive phenotype.

Steve Goodison

Papers

Local-Learning-Based Feature Selection for High-Dimensional Data Analysis

Bladder Cancer Detection and Monitoring: Assessment of Urine- and Blood-Based Marker Tests

Role of telomerase in cell senescence and oncogenesis

Expression of CXCL1 in human endothelial cells induces angiogenesis through the CXCR2 receptor and the ERK1/2 and EGF pathways.

Progressive loss of CD44 gene expression in invasive bladder cancer.