Deciphering multi-way interactions in the human genome.

Chromatin architecture, a key regulator of gene expression, can be inferred using chromatin contact data from chromosome conformation capture, or Hi-C. However, classical Hi-C does not preserve multi-way contacts. Here we use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organization in the human genome. We use hypergraph theory for data representation and analysis, and quantify higher order structures in neonatal fibroblasts, biopsied adult fibroblasts, and B lymphocytes. By integrating multi-way contacts with chromatin accessibility, gene expression, and transcription factor binding, we introduce a data-driven method to identify cell type-specific transcription clusters. We provide transcription factor-mediated functional building blocks for cell identity that serve as a global signature for cell types.

Human CD206+ macrophages associate with diabetes and adipose tissue lymphoid clusters.

Increased adipose tissue macrophages (ATMs) correlate with metabolic dysfunction in humans and are causal in development of insulin resistance in mice. Recent bulk and single-cell transcriptomics studies reveal a wide spectrum of gene expression signatures possible for macrophages that depends on context, but the signatures of human ATM subtypes are not well defined in obesity and diabetes. We profiled 3 prominent ATM subtypes from human adipose tissue in obesity and determined their relationship to type 2 diabetes. Visceral adipose tissue (VAT) and s.c. adipose tissue (SAT) samples were collected from diabetic and nondiabetic obese participants to evaluate cellular content and gene expression. VAT CD206+CD11c- ATMs were increased in diabetic participants, were scavenger receptor-rich with low intracellular lipids, secreted proinflammatory cytokines, and diverged significantly from 2 CD11c+ ATM subtypes, which were lipid-laden, were lipid antigen presenting, and overlapped with monocyte signatures. Furthermore, diabetic VAT was enriched for CD206+CD11c- ATM and inflammatory signatures, scavenger receptors, and MHC II antigen presentation genes. VAT immunostaining found CD206+CD11c- ATMs concentrated in vascularized lymphoid clusters adjacent to CD206-CD11c+ ATMs, while CD206+CD11c+ were distributed between adipocytes. Our results show ATM subtype-specific profiles that uniquely contribute to the phenotypic variation in obesity.

Functional organization of the maternal and paternal human 4D Nucleome.

Every human somatic cell inherits a maternal and a paternal genome, which work together to give rise to cellular phenotypes. However, the allele-specific relationship between gene expression and genome structure through the cell cycle is largely unknown. By integrating haplotype-resolved genome-wide chromosome conformation capture, mature and nascent mRNA, and protein binding data from a B lymphoblastoid cell line, we investigate this relationship both globally and locally. We introduce the maternal and paternal 4D Nucleome, enabling detailed analysis of the mechanisms and dynamics of genome structure and gene function for diploid organisms. Our analyses find significant coordination between allelic expression biases and local genome conformation, and notably absent expression bias in universally essential cell cycle and glycolysis genes. We propose a model in which coordinated biallelic expression reflects prioritized preservation of essential gene sets.

Understanding memory B cell selection.

The mammalian adaptive immune system has evolved over millions of years to become an incredibly effective defense against foreign antigens. The adaptive immune system's humoral response creates plasma B cells and memory B cells, each with their own immunological objectives. The affinity maturation process is widely viewed as a heuristic to solve the global optimization problem of finding B cells with high affinity to the antigen. However, memory B cells appear to be purposely selected earlier in the affinity maturation process and have lower affinity. We propose that this memory B cell selection process may be an approximate solution to two optimization problems: optimizing for affinity to similar antigens in the future despite mutations or other minor differences, and optimizing to warm start the generation of plasma B cells in the future. We use simulations to provide evidence for our hypotheses, taking into account data showing that certain B cell mutations are more likely than others. Our findings are consistent with memory B cells having high-affinity to mutated antigens, but do not provide strong evidence that memory B cells will be more useful than selected naive B cells for seeding the secondary germinal centers.

4DNvestigator: time series genomic data analysis toolbox.

Data on genome organization and output over time, or the 4D Nucleome (4DN), require synthesis for meaningful interpretation. Development of tools for the efficient integration of these data is needed, especially for the time dimension. We present the '4DNvestigator', a user-friendly network-based toolbox for the analysis of time series genome-wide genome structure (Hi-C) and gene expression (RNA-seq) data. Additionally, we provide methods to quantify network entropy, tensor entropy, and statistically significant changes in time series Hi-C data at different genomic scales.

Algorithm for cellular reprogramming.

The day we understand the time evolution of subcellular events at a level of detail comparable to physical systems governed by Newton's laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology. With data-guided frameworks we can develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. Here we describe an approach for optimizing the use of transcription factors (TFs) in cellular reprogramming, based on a device commonly used in optimal control. We construct an approximate model for the natural evolution of a cell-cycle-synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points during the cell cycle. To arrive at a model of moderate complexity, we cluster gene expression based on division of the genome into topologically associating domains (TADs) and then model the dynamics of TAD expression levels. Based on this dynamical model and additional data, such as known TF binding sites and activity, we develop a methodology for identifying the top TF candidates for a specific cellular reprogramming task. Our data-guided methodology identifies a number of TFs previously validated for reprogramming and/or natural differentiation and predicts some potentially useful combinations of TFs. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes.