Sex differences in the functional topography of association networks in youth

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy (P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

Dissolved organic matter (DOM) quality drives biogeographic patterns of soil bacterial communities and their association networks in semi-arid regions

It is of great interest to elucidate the biogeographic patterns of soil microorganisms and their driving forces, which is fundamental to predicting alterations in microbial-mediated functions arising from environment changes. Although dissolved organic matter (DOM) represents an important resource for soil microorganisms, knowledge of how its quality affects microbial biogeography is limited. Here, we characterized soil bacterial communities and DOM quality in 45 soil samples collected from a 1500-km sampling transect through semi-arid regions in northern China which are currently suffering great pressure from climate change, using Illumina Miseq sequencing and fluorescence spectroscopy, respectively. We found that DOM quality (i.e. the source of DOM and the humification degree of DOM) had profound shaping influence on the biogeographic patterns exhibited by bacterial diversity, community composition and association networks. Specifically, the composition of bacteria community closely associated with DOM quality. Plant-derived DOM sustained higher bacterial diversity relative to microbial-derived DOM. Meanwhile, bacterial diversity linearly increased with increasing humification degree of DOM. Additionally, plant-derived DOM was observed to foster more complex bacterial association networks with less competition. Together, our work contributes to the factors underlying biogeographic patterns not only of bacterial diversity, community composition but also of their association networks and reports previously undocumented important role of DOM quality in shaping these patterns.

Cancers in Agreement? Exploring the Cross-Talk of Cancer Metabolomic and Transcriptomic Landscapes Using Publicly Available Data

Simple Summary

Changes in metabolism are a well-known characteristic of cancer cells. Different cancer types are unique in their genetic aspects, but also in their metabolism, which is in turn, governed by genetics. The aim of our study was to find these differences in metabolic behavior across different cancer types and uncovering intersections between gene expression and metabolic deregulations. We scoured the public domain for metabolomics and transcriptomics data from clinical profiling studies to perform a comprehensive comparison study. By combining evidence from both the genetic and the metabolic aspects, we described the most prominently aberrated pathways across eight different cancer types together with their metabolomic and transcriptomics similarities.

Abstract

One of the major hallmarks of cancer is the derailment of a cell’s metabolism. The multifaceted nature of cancer and different cancer types is transduced by both its transcriptomic and metabolomic landscapes. In this study, we re-purposed the publicly available transcriptomic and metabolomics data of eight cancer types (breast, lung, gastric, renal, liver, colorectal, prostate, and multiple myeloma) to find and investigate differences and commonalities on a pathway level among different cancer types. Topological analysis of inferred graphical Gaussian association networks showed that cancer was strongly defined in genetic networks, but not in metabolic networks. Using different statistical approaches to find significant differences between cancer and control cases, we highlighted the difficulties of high-level data-merging and in using statistical association networks. Cancer transcriptomics and metabolomics and landscapes were characterized by changed macro-molecule production, however, only major metabolic deregulations with highly impacted pathways were found in liver cancer. Cell cycle was enriched in breast, liver, and colorectal cancer, while breast and lung cancer were distinguished by highly enriched oncogene signaling pathways. A strong inflammatory response was observed in lung cancer and, to some extent, renal cancer. This study highlights the necessity of combining different omics levels to obtain a better description of cancer characteristics.

Seasonal Niche Partitioning of Surface Temperate Open Ocean Prokaryotic Communities

Surface microbial communities are exposed to seasonally changing environmental conditions, resulting in recurring patterns of community composition. However, knowledge on temporal dynamics of open ocean microbial communities remains scarce. Seasonal patterns and associations of taxa and oligotypes from surface and chlorophyll maximum layers in the western Mediterranean Sea were studied over a 2-year period. Summer stratification versus winter mixing governed not only the prokaryotic community composition and diversity but also the temporal dynamics and co-occurrence association networks of oligotypes. Flavobacteriales, Rhodobacterales, SAR11, SAR86, and Synechococcales oligotypes exhibited contrasting seasonal dynamics, and consequently, specific microbial assemblages and potential inter-oligotype connections characterized the different seasons. In addition, oligotypes composition and dynamics differed between surface and deep chlorophyll maximum (DCM) prokaryotic communities, indicating depth-related environmental gradients as a major factor affecting association networks between closely related taxa. Taken together, the seasonal and depth specialization of oligotypes suggest temporal dynamics of community composition and metabolism, influencing ecosystem function and global biogeochemical cycles. Moreover, our results indicate highly specific associations between microbes, pointing to keystone ecotypes and fine-tuning of the microbes realized niche.

Using free association networks to extract characteristic patterns of affect dynamics

The dynamics of human affect in day-to-day life are an intrinsic part of human behaviour. Yet, it is difficult to observe and objectively measure how affect evolves over time with sufficient resolution. Here, we suggest an approach that combines free association networks with affect mapping, to gain insight into basic patterns of affect dynamics. This approach exploits the established connection in the literature between association networks and behaviour. Using extant rich data, we find consistent patterns of the dynamics of the valence and arousal dimensions of affect. First, we find that the individuals represented by the data tend to feel a constant pull towards an affect-neutral global equilibrium point in the valence-arousal space. The farther the affect is from that point, the stronger the pull. We find that the drift of affect exhibits high inertia, i.e. is slow-changing, but with occasional discontinuous jumps of valence. We further find that, under certain conditions, another metastable equilibrium point emerges on the network, but one which represents a much more negative and agitated state of affect. Finally, we demonstrate how the affect-coded association network can be used to identify useful or harmful trajectories of associative thoughts that otherwise are hard to extract.

A Combined PLS and Negative Binomial Regression Model for Inferring Association Networks from Next-Generation Sequencing Count Data

A major challenge of genomics data is to detect interactions displaying functional associations from large-scale observations. In this study, a new cPLS-algorithm combining partial least squares approach with negative binomial regression is suggested to reconstruct a genomic association network for high-dimensional next-generation sequencing count data. The suggested approach is applicable to the raw counts data, without requiring any further pre-processing steps. In the settings investigated, the cPLS-algorithm outperformed the two widely used comparative methods, graphical lasso, and weighted correlation network analysis. In addition, cPLS is able to estimate the full network for thousands of genes without major computational load. Finally, we demonstrate that cPLS is capable of finding biologically meaningful associations by analyzing an example data set from a previously published study to examine the molecular anatomy of the craniofacial development.

CoNet app: inference of biological association networks using Cytoscape

Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.

CoNet app: inference of biological association networks using Cytoscape

Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.

Differences in protein-protein association networks for lung adenocarcinoma: A retrospective study

Various methods to determine the connectivity scores between groups of proteins associated with lung adenocarcinoma are examined. Proteins act together to perform a wide range of functions within biological processes. Hence, identification of key proteins and their interactions within protein networks can provide invaluable information on disease mechanisms. Differential network analysis provides a means of identifying differences in the interactions among proteins between two networks. We use connectivity scores based on the method of partial least squares to quantify the strength of the interactions between each pair of proteins. These scores are then used to perform permutation-based statistical tests. This examines if there are significant differences between the network connectivity scores for individual proteins or classes of proteins. The expression data from a study on lung adenocarcinoma is used in this study. Connectivity scores are computed for a group of 109 subjects who were in the complete remission and as well as for a group of 51 subjects whose cancer had progressed. The distributions of the connectivity scores are similar for the two networks yet subtle but statistically significant differences have been identified and their impact discussed.