The mechanisms underlying the low efficiency of reprogramming somatic cells into induced pluripotent stem (iPS) cells are poorly understood. There is a clear need to study whether the reprogramming process itself compromises genomic integrity and, through this, the efficiency of iPS cell establishment. Using a high-resolution single nucleotide polymorphism array, we compared copy number variations (CNVs) of different passages of human iPS cells with their fibroblast cell origins and with human embryonic stem (ES) cells. Here we show that significantly more CNVs are present in early-passage human iPS cells than intermediate passage human iPS cells, fibroblasts or human ES cells. Most CNVs are formed de novo and generate genetic mosaicism in early-passage human iPS cells. Most of these novel CNVs rendered the affected cells at a selective disadvantage. Remarkably, expansion of human iPS cells in culture selects rapidly against mutated cells, driving the lines towards a genetic state resembling human ES cells.

Previous studies in Saccharomyces cerevisiae have demonstrated that cryptic promoters within coding regions activate transcription in particular mutants. We have performed a comprehensive analysis of cryptic transcription in order to identify factors that normally repress cryptic promoters, to determine the amount of cryptic transcription genome-wide, and to study the potential for expression of genetic information by cryptic transcription. Our results show that a large number of factors that control chromatin structure and transcription are required to repress cryptic transcription from at least 1,000 locations across the S. cerevisiae genome. Two results suggest that some cryptic transcripts are translated. First, as expected, many cryptic transcripts contain an ATG and an open reading frame of at least 100 codons. Second, several cryptic transcripts are translated into proteins. Furthermore, a subset of cryptic transcripts tested is transiently induced in wild-type cells following a nutritional shift, suggesting a possible physiological role in response to a change in growth conditions. Taken together, our results demonstrate that, during normal growth, the global integrity of gene expression is maintained by a wide range of factors and suggest that, under altered genetic or physiological conditions, the expression of alternative genetic information may occur.

Systems biology approaches can reveal intermediary levels of organization between genotype and phenotype that often underlie biological phenomena such as polygenic effects and protein dispensability. An important conceptualization is the module, which is loosely defined as a cohort of proteins that perform a dedicated cellular task. Based on a computational analysis of limited interaction datasets in the budding yeast Saccharomyces cerevisiae, it has been suggested that the global protein interaction network is segregated such that highly connected proteins, called hubs, tend not to link to each other. Moreover, it has been suggested that hubs fall into two distinct classes: “party” hubs are co-expressed and co-localized with their partners, whereas “date” hubs interact with incoherently expressed and diversely localized partners, and thereby cohere disparate parts of the global network. This structure may be compared with altocumulus clouds, i.e., cotton ball–like structures sparsely connected by thin wisps. However, this organization might reflect a small and/or biased sample set of interactions. In a multi-validated high-confidence (HC) interaction network, assembled from all extant S. cerevisiae interaction data, including recently available proteome-wide interaction data and a large set of reliable literature-derived interactions, we find that hub–hub interactions are not suppressed. In fact, the number of interactions a hub has with other hubs is a good predictor of whether a hub protein is essential or not. We find that date hubs are neither required for network tolerance to node deletion, nor do date hubs have distinct biological attributes compared to other hubs. Date and party hubs do not, for example, evolve at different rates. Our analysis suggests that the organization of global protein interaction network is highly interconnected and hence interdependent, more like the continuous dense aggregations of stratus clouds than the segregated configuration of altocumulus clouds. If the network is configured in a stratus format, cross-talk between proteins is potentially a major source of noise. In turn, control of the activity of the most highly connected proteins may be vital. Indeed, we find that a fluctuation in steady-state levels of the most connected proteins is minimized.

The distribution of genes on eukaryotic chromosomes is nonrandom, but the reasons behind this are not well understood. The commonly observed clustering of essential genes is a case in point. Here we model and test a new hypothesis. Essential proteins are unusual in that random fluctuations in abundance (noise) can be highly deleterious. We hypothesize that persistently open chromatin domains are sinks for essential genes, as they enable reduced noise by avoidance of transcriptional bursting associated with chromatin remodeling. Simulation of the model captures clustering and correctly predicts that (i) essential gene clusters are associated with low nucleosome occupancy (ii) noise-sensitive nonessential genes cluster with essential genes (iii) nonessential genes of similar knockout fitness are physically linked (iv) genes in domains rich in essential genes have low noise (v) essential genes are rare subtelomerically and (vi) essential gene clusters are preferentially conserved. We conclude that different noise characteristics of different genomic domains favors nonrandom gene positioning. This has implications for gene therapy and understanding transgenic phenotypes.

Background: The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference.

Mutations in human induced pluripotent stem cells (iPSCs) pose a risk for their clinical use due to preferential reprogramming of mutated founder cell and selection of mutations during maintenance of iPSCs in cell culture. It is unknown, however, if mutations in iPSCs are due to stress associated with oncogene expression during reprogramming. We performed whole exome sequencing of human foreskin fibroblasts and their derived iPSCs at two different passages. We found that in vitro passaging contributed 7% to the iPSC coding point mutation load, and ultradeep amplicon sequencing revealed that 19% of the mutations preexist as rare mutations in the parental fibroblasts suggesting that the remaining 74% of the mutations were acquired during cellular reprogramming. Simulation suggests that the mutation intensity during reprogramming is ninefold higher than the background mutation rate in culture. Thus the factor induced reprogramming stress contributes to a significant proportion of the mutation load of iPSCs. STEM CELLS 2012;30:435-440 Disclosure of potential conflicts of interest is found at the end of this article.

It has been claimed that proteins with more interaction partners (hubs) are both physiologically more important (i.e., less dispensable) and, owing to an assumed high density of binding sites, slow evolving. Not all analyses, however, support these results, probably because of biased and less-than reliable global protein interaction data. Here we provide the first examination of these issues using a comprehensive literature-curated dataset of well-substantiated protein interactions in Saccharomyces cerevisiae. Whereas use of less reliable yeast two-hybrid data alone can reject the possibility that local connectivity correlates with measures of dispensability, in higher quality datasets a relatively robust correlation is observed. In contrast, local connectivity does not correlate with the rate of protein evolution even in reliable datasets. This perhaps surprising lack of correlation with evolutionary rate appears in part to arise from the fact that hub proteins do not have a higher density of residues associated with binding. However, hub proteins do have at least one other set of unusual features, namely rapid turnover and regulation, as manifest in high mRNA decay rates and a large number of phosphorylation sites. This, we suggest, is an adaptation to minimize unwanted activation of pathways that might be mediated by adventitious binding to hubs, were they to actively persist longer than required at any given time point. We conclude that hub proteins are more important for cellular growth rate and under tight regulation but are not slow evolving.

No abstract