PyMix--the python mixture package--a tool for clustering of heterogeneous biological data

Within-patient phylogenetic reconstruction reveals early events in Barrett's Esophagus

Barrett's Esophagus is a neoplastic condition which progresses to esophageal adenocarcinoma in 5% of cases. Key events affecting the outcome likely occur before diagnosis of Barrett's and cannot be directly observed; we use phylogenetic analysis to infer such past events. We performed whole-genome sequencing on 4-6 samples from 40 cancer outcome and 40 noncancer outcome patients with Barrett's Esophagus, and inferred within-patient phylogenies of deconvoluted clonal lineages. Spatially proximate lineages clustered in the phylogenies, but temporally proximate ones did not. Lineages with inferred loss-of-function mutations in both copies of TP53 and CDKN2A showed enhanced spatial spread, whereas lineages with loss-of-function mutations in other frequently mutated loci did not. We propose a two-phase model with expansions of TP53 and CKDN2A mutant lineages during initial growth of the segment, followed by relative stasis. Subsequent to initial expansion, mutations in these loci as well as ARID1A and SMARCA4 may show a local selective advantage but do not expand far: The spatial structure of the Barrett's segment remains stable during surveillance even in patients who go on to cancer. We conclude that the cancer/noncancer outcome is strongly affected by early steps in formation of the Barrett's segment.

Toward a statistical description of methane emissions from arctic wetlands

Methane (CH₄) emissions from arctic tundra typically follow relations with soil temperature and water table depth, but these process-based descriptions can be difficult to apply to areas where no measurements exist. We formulated a description of the broader temporal flux pattern in the growing season based on two distinct CH₄ source components from slow and fast-turnover carbon. We used automatic closed chamber flux measurements from NE Greenland (74°N), W Greenland (64°N), and Svalbard (78°N) to identify and discuss these components. The temporal separation was well-suited in NE Greenland, where the hypothesized slow-turnover carbon peaked at a time significantly related to the timing of snowmelt. The temporally wider component from fast-turnover carbon dominated the emissions in W Greenland and Svalbard. Altogether, we found no dependence of the total seasonal CH₄ budget to the timing of snowmelt, and warmer sites and years tended to yield higher CH₄ emissions.

Expression-based segmentation of the Drosophila genome

Background

It is generally accepted that gene order in eukaryotes is nonrandom, with adjacent genes often sharing expression patterns across tissues, and that this organization may be important for gene regulation. Here we describe a novel method, based on an explicit probability model instead of correlation analysis, for identifying coordinately expressed gene clusters (‘coexpression segments’), apply it to Drosophila melanogaster, and look for epigenetic associations using publicly available data.

Results

We find that two-thirds of Drosophila genes fall into multigenic coexpression segments, and that such segments are of two main types, housekeeping and tissue-restricted. Consistent with correlation-based studies, we find that adjacent genes within the same segment tend to be physically closer to each other than to the adjacent genes in different segments, and that tissue-restricted segments are enriched for testis-expressed genes. Our segmentation pattern correlates with Hi-C based physical interaction domains, but segments are generally much smaller than domains. Intersegment regions (including those which do not correspond to physical domain boundaries) are enriched for insulator binding sites.

Conclusions

We describe a novel approach for identifying coexpression clusters that does not require arbitrary cutoff values or heuristics, and find that coexpression of adjacent genes is widespread in the Drosophila genome. Coexpression segments appear to reflect a level of regulatory organization related to, but below that of physical interaction domains, and depending in part on insulator binding.

Pain modulators regulate the dynamics of PKA-RII phosphorylation in subgroups of sensory neurons

Knowledge about the molecular structure of protein kinase A (PKA) isoforms is substantial. In contrast, the dynamics of PKA isoform activity in living primary cells has not been investigated in detail. Using a high content screening microscopy approach, we identified the RIIβ subunit of PKA-II to be predominantly expressed in a subgroup of sensory neurons. The RIIβ-positive subgroup included most neurons expressing nociceptive markers (TRPV1, NaV1.8, CGRP, IB4) and responded to pain-eliciting capsaicin with calcium influx. Isoform-specific PKA reporters showed in sensory-neuron-derived F11 cells that the inflammatory mediator PGE₂ specifically activated PKA-II but not PKA-I. Accordingly, pain-sensitizing inflammatory mediators and activators of PKA increased the phosphorylation of RII subunits (pRII) in subgroups of primary sensory neurons. Detailed analyses revealed basal pRII to be regulated by the phosphatase PP2A. Increase of pRII was followed by phosphorylation of CREB in a PKA-dependent manner. Thus, we propose RII phosphorylation to represent an isoform-specific readout for endogenous PKA-II activity in vivo, suggest RIIβ as a novel nociceptive subgroup marker, and extend the current model of PKA-II activation by introducing a PP2A-dependent basal state.

Inferring epigenetic and transcriptional regulation during blood cell development with a mixture of sparse linear models

MOTIVATION

Blood cell development is thought to be controlled by a circuit of transcription factors (TFs) and chromatin modifications that determine the cell fate through activating cell type-specific expression programs. To shed light on the interplay between histone marks and TFs during blood cell development, we model gene expression from regulatory signals by means of combinations of sparse linear regression models.

RESULTS

The mixture of sparse linear regression models was able to improve the gene expression prediction in relation to the use of a single linear model. Moreover, it performed an efficient selection of regulatory signals even when analyzing all TFs with known motifs (>600). The method identified interesting roles for histone modifications and a selection of TFs related to blood development and chromatin remodelling.

AVAILABILITY

The method and datasets are available from http://www.cin.ufpe.br/~igcf/SparseMix.

CONTACT

igcf@cin.ufpe.br

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The protein interaction network of extracellular vesicles derived from human colorectal cancer cells

Various mammalian cells including tumor cells secrete extracellular vesicles (EVs), otherwise known as exosomes and microvesicles. EVs are nanosized bilayered proteolipids and play multiple roles in intercellular communication. Although many vesicular proteins have been identified, their functional interrelationships and the mechanisms of EV biogenesis remain unknown. By interrogating proteomic data using systems approaches, we have created a protein interaction network of human colorectal cancer cell-derived EVs which comprises 1491 interactions between 957 vesicular proteins. We discovered that EVs have well-connected clusters with several hub proteins similar to other subcellular networks. We also experimentally validated that direct protein interactions between cellular proteins may be involved in protein sorting during EV formation. Moreover, physically and functionally interconnected protein complexes form functional modules involved in EV biogenesis and functions. Specifically, we discovered that SRC signaling plays a major role in EV biogenesis, and confirmed that inhibition of SRC kinase decreased the intracellular biogenesis and cell surface release of EVs. Our study provides global insights into the cargo-sorting, biogenesis, and pathophysiological roles of these complex extracellular organelles.

Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models

BACKGROUND

The differentiation process from stem cells to fully differentiated cell types is controlled by the interplay of chromatin modifications and transcription factor activity. Histone modifications or transcription factors frequently act in a multi-functional manner, with a given DNA motif or histone modification conveying both transcriptional repression and activation depending on its location in the promoter and other regulatory signals surrounding it.

RESULTS

To account for the possible multi functionality of regulatory signals, we model the observed gene expression patterns by a mixture of linear regression models. We apply the approach to identify the underlying histone modifications and transcription factors guiding gene expression of differentiated CD4+ T cells. The method improves the gene expression prediction in relation to the use of a single linear model, as often used by previous approaches. Moreover, it recovered the known role of the modifications H3K4me3 and H3K27me3 in activating cell specific genes and of some transcription factors related to CD4+ T differentiation.

Length control of the injectisome needle requires only one molecule of Yop secretion protein P (YscP)

The needle length of the Yersinia spp. injectisome is determined by Yop secretion protein P (YscP), an early substrate of the injectisome itself. There is a linear correlation between the length of YscP and the length of the needle, suggesting that YscP acts as a molecular ruler. However, it is not known whether one single molecule of YscP suffices to control the length of one needle or whether several molecules of YscP are exported in alternation with the needle subunit YscF until the needle length matches the ruler length, which would stop needle growth. To address this question, three different strains expressing simultaneously a short and a long version of YscP were engineered. The experimentally obtained needle length distribution was compared with the distributions predicted by stochastic modeling of the various possible scenarios. The experimental data are compatible with the single ruler model and not with the scenarios involving more than one ruler per needle.