Weak and uneven associations of home, neighborhood and school environments with stress hormone output across multiple time scales, bioRxiv, 2019-07-18

ABSTRACTThe progression of lifelong trajectories of socioeconomic inequalities in health and mortality begins in childhood. Dysregulation in cortisol, a stress hormone that is the primary output of the hypothalamus-pituitary-adrenal (HPA) axis, has been hypothesized to be a mechanism for how early environmental adversity compromises health. However, despite the popularity of cortisol as a biomarker for stress and adversity, little is known about whether cortisol output differs in children being raised in socioeconomically disadvantaged environments. Here, we show that there are few differences between advantaged and disadvantaged children in their cortisol output. In 8- to 14-year-old children from the population-based Texas Twin Project, we measured cortisol output at three different time-scales (1) diurnal fluctuation in salivary cortisol (n = 400), (2) salivary cortisol reactivity and recovery after exposure to the Trier Social Stress Test (n = 444), and (3) and cortisol concentration in hair (n = 1,210). These measures converged on two moderately correlated, yet distinguishable, dimensions of HPA function. We then tested differences in cortisol output across nine aspects of social disadvantage at the home (e.g., family socioeconomic status), school (e.g., average levels of academic achievement), and neighborhood (e.g., concentrated poverty). Children living in neighborhoods with higher concentrated poverty had higher diurnal cortisol output, as measured in saliva; otherwise, child cortisol output was unrelated to any other aspect of social disadvantage. Overall, we find limited support for alteration in HPA axis functioning as a general mechanism for the health consequences of socioeconomic inequality in childhood.

biorxiv physiology 100-200-users 2019

Compartment-dependent chromatin interaction dynamics revealed by liquid chromatin Hi-C, bioRxiv, 2019-07-17

SUMMARYChromosomes are folded so that active and inactive chromatin domains are spatially segregated. Compartmentalization is thought to occur through polymer phasemicrophase separation mediated by interactions between loci of similar type. The nature and dynamics of these interactions are not known. We developed liquid chromatin Hi-C to map the stability of associations between loci. Before fixation and Hi-C, chromosomes are fragmented removing the strong polymeric constraint to enable detection of intrinsic locus-locus interaction stabilities. Compartmentalization is stable when fragments are over 10-25 kb. Fragmenting chromatin into pieces smaller than 6 kb leads to gradual loss of genome organization. Dissolution kinetics of chromatin interactions vary for different chromatin domains. Lamin-associated domains are most stable, while interactions among speckle and polycomb-associated loci are more dynamic. Cohesin-mediated loops dissolve after fragmentation, possibly because cohesin rings slide off nearby DNA ends. Liquid chromatin Hi-C provides a genome-wide view of chromosome interaction dynamics.Highlights<jatslist list-type=bullet><jatslist-item>Liquid chromatin Hi-C detects chromatin interaction dissociation rates genome-wide<jatslist-item><jatslist-item>Chromatin conformations in distinct nuclear compartments differ in stability<jatslist-item><jatslist-item>Stable heterochromatic associations are major drivers of chromatin phase separation<jatslist-item><jatslist-item>CTCF-CTCF loops are stabilized by encirclement of loop bases by cohesin rings<jatslist-item>

biorxiv genomics 100-200-users 2019

DNA mismatches reveal widespread conformational penalties in protein-DNA recognition, bioRxiv, 2019-07-17

ABSTRACTTranscription-factor (TF) proteins recognize specific genomic sequences, despite an overwhelming excess of non-specific DNA, to regulate complex gene expression programs1–3. While there have been significant advances in understanding how DNA sequence and shape contribute to recognition, some fundamental aspects of protein-DNA binding remain poorly understood2,3. Many DNA-binding proteins induce changes in the DNA structure outside the intrinsic B-DNA envelope. How the energetic cost associated with distorting DNA contributes to recognition has proven difficult to study and measure experimentally because the distorted DNA structures exist as low-abundance conformations in the naked B-DNA ensemble4–10. Here, we use a novel high-throughput assay called SaMBA (Saturation Mismatch-Binding Assay) to investigate the role of DNA conformational penalties in TF-DNA recognition. The approach introduces mismatched base-pairs (i.e. mispairs) within TF binding sites to pre-induce a variety of DNA structural distortions much larger than those induced by changes in Watson-Crick sequence. Strikingly, while most mismatches either weakened TF binding (~70%) or had negligible effects (~20%), approximately 10% of mismatches increased binding and at least one mismatch was found that increased the binding affinity for each of 21 examined TFs. Mismatches also converted sites from the non-specific affinity range into specific sites, and high-affinity sites into “super-sites” stronger than any known canonical binding site. These findings reveal a complex binding landscape that cannot be explained based on DNA sequence alone. Analysis of crystal structures together with NMR and molecular dynamics simulations revealed that many of the mismatches that increase binding induce distortions similar to those induced by TF binding, thus pre-paying some of the energetic cost to deform the DNA. Our work indicates that conformational penalties are a major determinant of protein-DNA recognition, and reveals mechanisms by which mismatches can recruit TFs and thus modulate replication and repair activities in the cell11,12.

biorxiv biophysics 0-100-users 2019

Gene networks with transcriptional bursting recapitulate rare transient coordinated expression states in cancer, bioRxiv, 2019-07-17

SUMMARYNon-genetic transcriptional variability at the single-cell level is a potential mechanism for therapy resistance in melanoma. Specifically, rare subpopulations of melanoma cells occupy a transient pre-resistant state characterized by coordinated high expression of several genes. Importantly, these rare cells are able to survive drug treatment and develop resistance. How might these extremely rare states arise and disappear within the population? It is unclear whether the canonical stochastic models of probabilistic transcriptional pulsing can explain this behavior, or if it requires special, hitherto unidentified molecular mechanisms. Here we use mathematical modeling to show that a minimal network comprising of transcriptional bursting and interactions between genes can give rise to rare coordinated high states. We next show that although these states occur across networks of different sizes, they depend strongly on three (out of seven) model parameters and require network connectivity to be ≤ 6. Interestingly, we find that while entry into the rare coordinated high state is initiated by a long transcriptional burst that also triggers entry of other genes, the exit from it occurs through the independent inactivation of individual genes. Finally, our model predicts that increased network connectivity can lead to transcriptionally stable states, which we verify using network inference analysis of experimental data. In sum, we demonstrate that established principles of gene regulation are sufficient to describe this new class of rare cell variability and argue for its general existence in other biological contexts.

biorxiv systems-biology 0-100-users 2019

Human Genome Assembly in 100 Minutes, bioRxiv, 2019-07-17

AbstractDe novo genome assembly provides comprehensive, unbiased genomic information and makes it possible to gain insight into new DNA sequences not present in reference genomes. Many de novo human genomes have been published in the last few years, leveraging a combination of inexpensive short-read and single-molecule long-read technologies. As long-read DNA sequencers become more prevalent, the computational burden of generating assemblies persists as a critical factor. The most common approach to long-read assembly, using an overlap-layout-consensus (OLC) paradigm, requires all-to-all read comparisons, which quadratically scales in computational complexity with the number of reads. We assert that recently achievements in sequencing technology (i.e. with accuracy ~99% and read length ~10-15k) enables a fundamentally better strategy for OLC that is effectively linear rather than quadratic. Our genome assembly implementation, Peregrine uses sparse hierarchical minimizers (SHIMMER) to index reads thereby avoiding the need for an all-to-all read comparison step. Peregrine can assemble 30x human PacBio CCS read datasets in less than 30 CPU hours and around 100 wall-clock minutes to a high contiguity assembly (N50 &gt; 20Mb). The continued advance of sequencing technologies coupled with the Peregrine assembler enables routine generation of human de novo assemblies. This will allow for population scale measurements of more comprehensive genomic variations -- beyond SNPs and small indels -- as well as novel applications requiring rapid access to de novo assemblies.

biorxiv bioinformatics 100-200-users 2019

 

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