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1. Comparative developmental genomics of sex-biased gene expression in early embryogenesis across mammals

   https://doi.org/10.1186/s13293-023-00520-z  

   Richardson, Victorya ; Engel, Nora ; Kulathinal, Rob J. ( May 2023 , Biology of Sex Differences)

   Mammalian gonadal sex is determined by the presence or absence of a Y chromosome and the subsequent production of sex hormones contributes to secondary sexual differentiation. However, sex chromosome-linked genes encoding dosage-sensitive transcription and epigenetic factors are expressed well before gonad formation and have the potential to establish sex-biased expression that persists beyond the appearance of gonadal hormones. Here, we apply a comparative bioinformatics analysis on a pair of published single-cell datasets from mouse and human during very early embryogenesis—from two-cell to pre-implantation stages—to characterize sex-specific signals and to assess the degree of conservation among early acting sex-specific genes and pathways.

   Clustering and regression analyses of gene expression across samples reveal that sex initially plays a significant role in overall gene expression patterns at the earliest stages of embryogenesis which potentially may be the byproduct of signals from male and female gametes during fertilization. Although these transcriptional sex effects rapidly diminish, sex-biased genes appear to form sex-specific protein–protein interaction networks across pre-implantation stages in both mammals providing evidence that sex-biased expression of epigenetic enzymes may establish sex-specific patterns that persist beyond pre-implantation. Non-negative matrix factorization (NMF) on male and female transcriptomes generated clusters of genes with similar expression patterns across sex and developmental stages, including post-fertilization, epigenetic, and pre-implantation ontologies conserved between mouse and human. While the fraction of sex-differentially expressed genes (sexDEGs) in early embryonic stages is similar and functional ontologies are conserved, the genes involved are generally different in mouse and human.

   This comparative study uncovers much earlier than expected sex-specific signals in mouse and human embryos that pre-date hormonal signaling from the gonads. These early signals are diverged with respect to orthologs yet conserved in terms of function with important implications in the use of genetic models for sex-specific disease.

    

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2. Hierarchical Modular Structure of the Drosophila Connectome

   https://doi.org/10.1523/JNEUROSCI.0134-23.2023  

   Kunin, Alexander B. ; Guo, Jiahao ; Bassler, Kevin E. ; Pitkow, Xaq ; Josić, Krešimir ( August 2023 , The Journal of Neuroscience)

   The structure of neural circuitry plays a crucial role in brain function. Previous studies of brain organization generally had to trade off between coarse descriptions at a large scale and fine descriptions on a small scale. Researchers have now reconstructed tens to hundreds of thousands of neurons at synaptic resolution, enabling investigations into the interplay between global, modular organization, and cell type-specific wiring. Analyzing data of this scale, however, presents unique challenges. To address this problem, we applied novel community detection methods to analyze the synapse-level reconstruction of an adult femaleDrosophila melanogasterbrain containing >20,000 neurons and 10 million synapses. Using a machine-learning algorithm, we find the most densely connected communities of neurons by maximizing a generalized modularity density measure. We resolve the community structure at a range of scales, from large (on the order of thousands of neurons) to small (on the order of tens of neurons). We find that the network is organized hierarchically, and larger-scale communities are composed of smaller-scale structures. Our methods identify well-known features of the fly brain, including its sensory pathways. Moreover, focusing on specific brain regions, we are able to identify subnetworks with distinct connectivity types. For example, manual efforts have identified layered structures in the fan-shaped body. Our methods not only automatically recover this layered structure, but also resolve finer connectivity patterns to downstream and upstream areas. We also find a novel modular organization of the superior neuropil, with distinct clusters of upstream and downstream brain regions dividing the neuropil into several pathways. These methods show that the fine-scale, local network reconstruction made possible by modern experimental methods are sufficiently detailed to identify the organization of the brain across scales, and enable novel predictions about the structure and function of its parts.

   Significance StatementThe Hemibrain is a partial connectome of an adult femaleDrosophila melanogasterbrain containing >20,000 neurons and 10 million synapses. Analyzing the structure of a network of this size requires novel and efficient computational tools. We applied a new community detection method to automatically uncover the modular structure in the Hemibrain dataset by maximizing a generalized modularity measure. This allowed us to resolve the community structure of the fly hemibrain at a range of spatial scales revealing a hierarchical organization of the network, where larger-scale modules are composed of smaller-scale structures. The method also allowed us to identify subnetworks with distinct cell and connectivity structures, such as the layered structures in the fan-shaped body, and the modular organization of the superior neuropil. Thus, network analysis methods can be adopted to the connectomes being reconstructed using modern experimental methods to reveal the organization of the brain across scales. This supports the view that such connectomes will allow us to uncover the organizational structure of the brain, which can ultimately lead to a better understanding of its function.

    

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3. GLIDER: function prediction from GLIDE-based neighborhoods

   https://doi.org/10.1093/bioinformatics/btac322  

   Devkota, Kapil ; Schmidt, Henri ; Werenski, Matt ; Murphy, James M. ; Erden, Mert ; Arsenescu, Victor ; Cowen, Lenore J. ; Valencia, ed., Alfonso ( May 2022 , Bioinformatics)

   Protein function prediction, based on the patterns of connection in a protein–protein interaction (or association) network, is perhaps the most studied of the classical, fundamental inference problems for biological networks. A highly successful set of recent approaches use random walk-based low-dimensional embeddings that tend to place functionally similar proteins into coherent spatial regions. However, these approaches lose valuable local graph structure from the network when considering only the embedding. We introduce GLIDER, a method that replaces a protein–protein interaction or association network with a new graph-based similarity network. GLIDER is based on a variant of our previous GLIDE method, which was designed to predict missing links in protein–protein association networks, capturing implicit local and global (i.e. embedding-based) graph properties.

   GLIDER outperforms competing methods on the task of predicting GO functional labels in cross-validation on a heterogeneous collection of four human protein–protein association networks derived from the 2016 DREAM Disease Module Identification Challenge, and also on three different protein–protein association networks built from the STRING database. We show that this is due to the strong functional enrichment that is present in the local GLIDER neighborhood in multiple different types of protein–protein association networks. Furthermore, we introduce the GLIDER graph neighborhood as a way for biologists to visualize the local neighborhood of a disease gene. As an application, we look at the local GLIDER neighborhoods of a set of known Parkinson’s Disease GWAS genes, rediscover many genes which have known involvement in Parkinson’s disease pathways, plus suggest some new genes to study.

   All code is publicly available and can be accessed here: https://github.com/kap-devkota/GLIDER.

   Supplementary data are available at Bioinformatics online.

    

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4. C3NA: correlation and consensus-based cross-taxonomy network analysis for compositional microbial data

   https://doi.org/10.1186/s12859-022-05027-9  

   Song, Kuncheng ; Zhou, Yi-Hui ( November 2022 , BMC Bioinformatics)

   Studying the co-occurrence network structure of microbial samples is one of the critical approaches to understanding the perplexing and delicate relationship between the microbe, host, and diseases. It is also critical to develop a tool for investigating co-occurrence networks and differential abundance analyses to reveal the disease-related taxa–taxa relationship. In addition, it is also necessary to tighten the co-occurrence network into smaller modules to increase the ability for functional annotation and interpretability of  these taxa-taxa relationships.  Also, it is critical to retain the phylogenetic relationship among the taxa to identify differential abundance patterns, which can be used to resolve contradicting functions reported by different studies.

   In this article, we present Correlation and Consensus-based Cross-taxonomy Network Analysis (C3NA), a user-friendly R package for investigating compositional microbial sequencing data to identify and compare co-occurrence patterns across different taxonomic levels. C3NA contains two interactive graphic user interfaces (Shiny applications), one of them dedicated to the comparison between two diagnoses, e.g., disease versus control. We used C3NA to analyze two well-studied diseases, colorectal cancer, and Crohn’s disease. We discovered clusters of study and disease-dependent taxa that overlap with known functional taxa studied by other discovery studies and differential abundance analyses.

   C3NA offers a new microbial data analyses pipeline for refined and enriched taxa–taxa co-occurrence network analyses, and the usability was further expanded via the built-in Shiny applications for interactive investigation.

    

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5. A subset of topologically associating domains fold into mesoscale core-periphery networks

   https://doi.org/10.1038/s41598-019-45457-9  

   Huang, Harvey ; Chen, Sunnia T. ; Titus, Katelyn R. ; Emerson, Daniel J. ; Bassett, Danielle S. ; Phillips-Cremins, Jennifer E. ( July 2019 , Scientific Reports)

   Mammalian genomes are folded into a hierarchy of compartments, topologically associating domains (TADs), subTADs, and long-range looping interactions. The higher-order folding patterns of chromatin contacts within TADs and how they localize to disease-associated single nucleotide variants (daSNVs) remains an open area of investigation. Here, we analyze high-resolution Hi-C data with graph theory to understand possible mesoscale network architecture within chromatin domains. We identify a subset of TADs exhibiting strong core-periphery mesoscale structure in embryonic stem cells, neural progenitor cells, and cortical neurons. Hyper-connected core nodes co-localize with genomic segments engaged in multiple looping interactions and enriched for occupancy of the architectural protein CCCTC binding protein (CTCF). CTCF knockdown andin silicodeletion of CTCF-bound core nodes disrupts core-periphery structure, whereasin silicomutation of cell type-specific enhancer or gene nodes has a negligible effect. Importantly, neuropsychiatric daSNVs are significantly more likely to localize with TADs folded into core-periphery networks compared to domains devoid of such structure. Together, our results reveal that a subset of TADs encompasses looping interactions connected into a core-periphery mesoscale network. We hypothesize that daSNVs in the periphery of genome folding networks might preserve global nuclear architecture but cause local topological and functional disruptions contributing to human disease. By contrast, daSNVs co-localized with hyper-connected core nodes might cause severe topological and functional disruptions. Overall, these findings shed new light into the mesoscale network structure of fine scale genome folding within chromatin domains and its link to common genetic variants in human disease.

    

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