Guang-Zhong Wang

Human-specific transcriptional networks in the brain

Understanding human-specific patterns of brain gene expression and regulation can provide key insights into human brain evolution and speciation. Here, we use next-generation sequencing, and Illumina and Affymetrix microarray platforms, to compare the transcriptome of human, chimpanzee, and macaque telencephalon. Our analysis reveals a predominance of genes differentially expressed within human frontal lobe and a striking increase in transcriptional complexity specific to the human lineage in the frontal lobe. In contrast, caudate nucleus gene expression is highly conserved. We also identify gene coexpression signatures related to either neuronal processes or neuropsychiatric diseases, including a human-specific module with CLOCK as its hub gene and another module enriched for neuronal morphological processes and genes coexpressed with FOXP2, a gene important for language evolution. These data demonstrate that transcriptional networks have undergone evolutionary remodeling even within a given brain region, providing a window through which to view the foundation of uniquely human cognitive capacities.

Correspondence between Resting-State Activity and Brain Gene Expression

The relationship between functional brain activity and gene expression has not been fully explored in the human brain. Here, we identify significant correlations between gene expression in the brain and functional activity by comparing fractional amplitude of low-frequency fluctuations (fALFF) from two independent human fMRI resting-state datasets to regional cortical gene expression from a newly generated RNA-seq dataset and two additional gene expression datasets to obtain robust and reproducible correlations. We find significantly more genes correlated with fALFF than expected by chance and identify specific genes correlated with the imaging signals in multiple expression datasets in the default mode network. Together, these data support a population-level relationship between regional steady-state brain gene expression and resting-state brain activity.

Cycling Transcriptional Networks Optimize Energy Utilization on a Genome Scale

Genes expressing circadian RNA rhythms are enriched for metabolic pathways, but the adaptive significance of cyclic gene expression remains unclear. We estimated the genome-wide synthetic and degradative cost of transcription and translation in three organisms and found that the cost of cycling genes is strikingly higher compared to non-cycling genes. Cycling genes are expressed at high levels and constitute the most costly proteins to synthesize in the genome. We demonstrate that metabolic cycling is accelerated in yeast grown under higher nutrient flux and the number of cycling genes increases ∼40%, which are achieved by increasing the amplitude and not the mean level of gene expression. These results suggest that rhythmic gene expression optimizes the metabolic cost of global gene expression and that highly expressed genes have been selected to be downregulated in a cyclic manner for energy conservation.

The Effects of Network Neighbours on Protein Evolution

Interacting proteins may often experience similar selection pressures. Thus, we may expect that neighbouring proteins in biological interaction networks evolve at similar rates. This has been previously shown for protein-protein interaction networks. Similarly, we find correlated rates of evolution of neighbours in networks based on co-expression, metabolism, and synthetic lethal genetic interactions. While the correlations are statistically significant, their magnitude is small, with network effects explaining only between 2% and 7% of the variation. The strongest known predictor of the rate of protein evolution remains expression level. We confirmed the previous observation that similar expression levels of neighbours indeed explain their similar evolution rates in protein-protein networks, and showed that the same is true for metabolic networks. In co-expression and synthetic lethal genetic interaction networks, however, neighbouring genes still show somewhat similar evolutionary rates even after simultaneously controlling for expression level, gene essentiality and gene length. Thus, similar expression levels and related functions (as inferred from co-expression and synthetic lethal interactions) seem to explain correlated evolutionary rates of network neighbours across all currently available types of biological networks.

Exploring the common molecular basis for the universal DNA mutation bias: Revival of Löwdin mutation model

Recently, numerous genome analyses revealed the existence of a universal G:C→A:T mutation bias in bacteria, fungi, plants and animals. To explore the molecular basis for this mutation bias, we examined the three well-known DNA mutation models, i.e., oxidative damage model, UV-radiation damage model and CpG hypermutation model. It was revealed that these models cannot provide a sufficient explanation to the universal mutation bias. Therefore, we resorted to a DNA mutation model proposed by Löwdin 40 years ago, which was based on inter-base double proton transfers (DPT). Since DPT is a fundamental and spontaneous chemical process and occurs much more frequently within GC pairs than AT pairs, Löwdin model offers a common explanation for the observed universal mutation bias and thus has broad biological implications.

Coexpression of Linked Gene Pairs Persists Long after Their Separation

In many eukaryotes, physically linked gene pairs tend to be coexpressed. However, it is still controversial to what extent this neighbor coexpression is maintained by selection and to what extent it is nonselective, purely mechanistic “leaky expression.” Here, we analyze expression patterns of gene pairs that have lost their linkage in the evolution of Saccharomyces cerevisiae since its last common ancestor with Kluyveromyces waltii or that were never linked in the S. cerevisiae lineage but became neighbors in a related yeast. We demonstrate that coexpression of many linked genes is retained long after their separation and is thus likely to be functionally important. In addition, unlinked gene pairs that recently became neighbors in other yeast species tend to be coexpressed in S. cerevisiae. This suggests that natural selection often favors chromosomal rearrangements in which coexpressed genes become neighbors. Contrary to previous suggestions, selectively favorable coexpression appears not to be restricted to bidirectional promoters.

A gene's ability to buffer variation is predicted by its fitness contribution and genetic interactions.

Background Many single-gene knockouts result in increased phenotypic (e.g., morphological) variability among the mutants offspring. This has been interpreted as an intrinsic ability of genes to buffer genetic and environmental variation. A phenotypic capacitor is a gene that appears to mask phenotypic variation: when knocked out, the offspring shows more variability than the wild type. Theory predicts that this phenotypic potential should be correlated with a genes knockout fitness and its number of negative genetic interactions. Based on experimentally measured phenotypic capacity, it was suggested that knockout fitness was unimportant, but that phenotypic capacitors tend to be hubs in genetic and physical interaction networks. Methodology/Principal Findings We re-analyse the available experimental data in a combined model, which includes knockout fitness and network parameters as well as expression level and protein length as predictors of phenotypic potential. Contrary to previous conclusions, we find that the strongest predictor is in fact haploid knockout fitness (responsible for 9% of the variation in phenotypic potential), with an additional contribution from the genetic interaction network (5%); once these two factors are taken into account, protein-protein interactions do not make any additional contribution to the variation in phenotypic potential. Conclusions/Significance We conclude that phenotypic potential is not a mysterious “emergent” property of cellular networks. Instead, it is very simply determined by the overall fitness reduction of the organism (which in its compromised state can no longer compensate for multiple factors that contribute to phenotypic variation), and by the number (and presumably nature) of genetic interactions of the knocked-out gene. In this light, Hsp90, the prototypical phenotypic capacitor, may not be representative: typical phenotypic capacitors are not direct “buffers” of variation, but are simply genes encoding central cellular functions.

Decoding human gene expression signatures in the brain.

The evolution of higher cognitive functions in humans is thought to be due, at least in part, to the molecular evolution of gene expression patterns specific to the human brain. In this article, we explore recent and past findings using comparative genomics in human and non-human primate brain to identify these novel human patterns. We suggest additional directions and lines of experimentation that should be taken to improve our understanding of these changes on the human lineage. Finally, we attempt to put into context these genomic changes with biological phenotypes and diseases in humans.

Does negative auto-regulation increase gene duplicability?

BackgroundA prerequisite for a duplication to spread through and persist in a given population is retaining expression of both gene copies. Yet changing a genes dosage is frequently detrimental to fitness. Consequently, dosage-sensitive genes are less likely to duplicate.However, in cases where the level of gene product is controlled, via negative feedback, by its own abundance, an increase in gene copy number can in principle be decoupled from an increase in protein while both copies remain expressed. Using data from the transcriptional networks of E. coli and S. cerevisiae, we test the hypothesis that genes under negative auto-regulation show enhanced duplicability.ResultsControlling for several known correlates of duplicability, we find no statistically significant support in either E. coli or S. cerevisiae that transcription factors under negative auto-regulation hold a duplicability advantage over transcription factors with no auto-regulation.ConclusionBased on the analysis of transcriptional networks in E. coli and S. cerevisiae, there is no evidence that negative auto-regulation has contributed, on a genome-wide scale, to the variability in gene family sizes in these species.

Improvement of Dscam homophilic binding affinity throughout Drosophila evolution.

BackgroundDrosophila Dscam1 is a cell-surface protein that plays important roles in neural development and axon tiling of neurons. It is known that thousands of isoforms bind themselves through specific homophilic interactions, a process which provides the basis for cellular self-recognition. Detailed biochemical studies of specific isoforms strongly suggest that homophilic binding, i.e. the formation of homodimers by identical Dscam1 isomers, is of great importance for the self-avoidance of neurons. Due to experimental limitations, it is currently impossible to measure the homophilic binding affinities for all 19,000 potential isoforms.ResultsHere we reconstructed the DNA sequences of an ancestral Dscam form (which likely existed approximately 40 ~ 50 million years ago) using a comparative genomic approach. On the basis of this sequence, we established a working model to predict the self-binding affinities of all isoforms in both the current and the ancestral genome, using machine-learning methods. Detailed computational analysis was performed to compare the self-binding affinities of all isoforms present in these two genomes. Our results revealed that 1) isoforms containing newly derived variable domains exhibit higher self-binding affinities than those with conserved domains, and 2) current isoforms display higher self-binding affinities than their counterparts in the ancient genome. As thousands of Dscam isoforms are needed for the self-avoidance of the neuron, we propose that an increase in self-binding affinity provides the basis for the successful evolution of the arthropod brain.ConclusionsOur data presented here provide an excellent model for future experimental studies of the binding behavior of Dscam isoforms. The results of our analysis indicate that evolution favored the rise of novel variable domains thanks to their higher self-binding affinities, rather than selection merely on the basis of simple expansion of isoform diversity, as that this particular selection process would have established the powerful mechanisms required for neuronal self-avoidance. Thus, we reveal here a new molecular mechanism for the successful evolution of arthropod brains.