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BackgroundEarly onset of type 2 diabetes and cardiovascular disease are common complications for women diagnosed with gestational diabetes. About half of the women with gestational diabetes develop postpartum prediabetes within 10 years of the index pregnancy. These women also have double the risk of developing cardiovascular disease than women without a history of gestational diabetes. Currently, there is no accurate way of knowing which women with gestational diabetes are likely to develop postpartum prediabetes. This study aims to predict the risk of postpartum prediabetes in women diagnosed with gestational diabetes.MethodsWe build a sparse logistic regression-based machine learning model to learn key variables significant for the prediction of postpartum prediabetes, from antenatal data with maternal anthropometric and biochemical variables as well as neonatal characteristics of 607 UK women diagnosed with gestational diabetes. We evaluate the performance of the proposed model in addition to other more advanced machine learning methods using established metrics such as the area under the receiver operating characteristic curve and specificity for pre-determined values of sensitivity. We use K-L divergence and information graphs to evaluate and compare different thresholds of classification for targeted screening options in resource-constrained settings. We also perform a decision curve analysis to study the net standardized benefit of our model compared to the universal screening approach.ResultsStrikingly, our sparse logistic regression approach selects only two variables as relevant but gives an area under the receiver operating characteristic curve of 0.72, outperforming all other methods. It can identify postpartum prediabetes in women with gestational diabetes using the Rule-in test with 92% specificity at an optimal probability threshold of 0.381 and using the Rule-out test with 92% sensitivity at an optimal probability threshold of 0.140.ConclusionWe propose a simple logistic regression model, which needs only the antenatal fasting glucose at OGTT and HbA1c soon after the diagnosis of GDM, to predict, with remarkable accuracy, the probability of postpartum prediabetes in women with gestational diabetes. We envision this to be a practical solution, which coupled with a targeted follow-up of high-risk women, could yield better cardiometabolic outcomes in women with a history of GDM.

Fetal growth is monitored periodically during pregnancy via ultrasound measurements of fetal dimensions such as femur length (FL), head circumference (HC), abdominal circumference (AC), and biparietal diameter (BPD). Multiple growth standards have been published for each of these, which are clinically used to place a fetus on a “growth chart”. These consist of percentile tables varying by weeks of gestation, computed from cohorts of “low-risk” women with healthy lifestyles, living conditions, and clinical parameters. Such charts are prescriptive of ideal growth, but not necessarily descriptive of diverse real-world populations where they may be used. Moreover, they are constructed by pooling all fetal measurements across the cohort, not based on a growth model, and therefore not necessarily predictive of growth of an individual fetus.We show that the Gompertz model, a standard model for constrained growth, with just three intuitive parameters, convincingly fits the growth of fetal ultrasound biometries. Two of these parameters—t0(the inflection time) andc(the rate of decrease of growth rate)—can be treated as universal to all fetuses, while the third parameterAcan be modeled as an overall scale parameter specific to each fetus, which captures the individual variation in growth. On our cohort of 817 pregnant women (“Seethapathy cohort”), we show that not only can the value ofAfor each fetus be inferred from ultrasound data available by the second or the third trimester, but the weight of the baby at delivery can also be predicted with remarkable accuracy using these inferred Gompertz parameters. A model trained on the Seethapathy cohort performs well in estimating the birth weight in an independent validation cohort of 365 women, demonstrating the predictive power of the model. Moreover, we find that deviation from Gompertz-like growth is linked to neonatal complications. Finally, we show that the Gompertz growth curve is a close fit to the standards from WHO, NICHD and INTERGROWTH, with the optimalt0andcclose to that in the Seethapathy cohort. We propose that the Gompertz formula be a basis for future growth standards, with almost all variation described by a single scale parameterA, which can serve either as a descriptor of mean or variance in population, or as a descriptor for growth of an individual fetus. Indeed, the formula is descriptive of typical growth, predictive of future growth, and may be used in prescriptive standards.

The establishment of centromeric chromatin and its propagation by the centromere-specific histone CENPA is mediated by epigenetic mechanisms in most eukaryotes. DNA replication origins, origin binding proteins, and replication timing of centromere DNA are important determinants of centromere function. The epigenetically regulated regional centromeres in the budding yeast Candida albicans have unique DNA sequences that replicate earliest in every chromosome and are clustered throughout the cell cycle. In this study, the genome-wide occupancy of the replication initiation protein Orc4 reveals its abundance at all centromeres in C. albicans. Orc4 is associated with four different DNA sequence motifs, one of which coincides with tRNA genes (tDNA) that replicate early and cluster together in space. Hi-C combined with genome-wide replication timing analyses identify that early replicating Orc4-bound regions interact with themselves stronger than with late replicating Orc4-bound regions. We simulate a polymer model of chromosomes of C. albicans and propose that the early replicating and highly enriched Orc4-bound sites preferentially localize around the clustered kinetochores. We also observe that Orc4 is constitutively localized to centromeres, and both Orc4 and the helicase Mcm2 are essential for cell viability and CENPA stability in C. albicans. Finally, we show that new molecules of CENPA are recruited to centromeres during late anaphase/telophase, which coincides with the stage at which the CENPA-specific chaperone Scm3 localizes to the kinetochore. We propose that the spatiotemporal localization of Orc4 within the nucleus, in collaboration with Mcm2 and Scm3, maintains centromeric chromatin stability and CENPA recruitment in C. albicans.

Transcription factors (TFs) and their binding sites have evolved to interact cooperatively or competitively with each other. Here we examine in detail, across multiple cell lines, such cooperation or competition among TFs both in sequential and spatial proximity (using chromatin conformation capture assays) on one hand, and based on both in vivo binding as well as TF binding motifs on the other. We ascertain significantly co-occurring (“attractive”) or avoiding (“repulsive”) TF pairs using robust randomized models that retain the essential characteristics of the experimental data. Across human cell lines TFs organize into two groups, with intra-group attraction and inter-group repulsion. This is true for both sequential and spatial proximity, as well as for both in vivo binding and motifs. Attractive TF pairs exhibit significantly more physical interactions suggesting an underlying mechanism. The two TF groups differ significantly in their genomic and network properties, as well in their function—while one group regulates housekeeping function, the other potentially regulates lineage-specific functions, that are disrupted in cancer. We also show that weaker binding sites tend to occur in spatially interacting regions of the genome. Our results suggest a complex pattern of spatial cooperativity of TFs that has evolved along with the genome to support housekeeping and lineage-specific functions.

The thermotolerant multidrug-resistant ascomycete Candida auris rapidly emerged since 2009 causing systemic infections worldwide and simultaneously evolved in different geographical zones. The molecular events that orchestrated this sudden emergence of the killer fungus remain mostly elusive. Here, we identify centromeres in C. auris and related species, using a combined approach of chromatin immunoprecipitation and comparative genomic analyses. We find that C. auris and multiple other species in the Clavispora/Candida clade shared a conserved small regional GC-poor centromere landscape lacking pericentromeres or repeats. Further, a centromere inactivation event led to karyotypic alterations in this species complex. Interspecies genome analysis identified several structural chromosomal changes around centromeres. In addition, centromeres are found to be rapidly evolving loci among the different geographical clades of the same species of C. auris. Finally, we reveal an evolutionary trajectory of the unique karyotype associated with clade 2 that consists of the drug-susceptible isolates of C. auris. IMPORTANCECandida auris, the killer fungus, emerged as different geographical clades, exhibiting multidrug resistance and high karyotype plasticity. Chromosomal rearrangements are known to play key roles in the emergence of new species, virulence, and drug resistance in pathogenic fungi. Centromeres, the genomic loci where microtubules attach to separate the sister chromatids during cell division, are known to be hot spots of breaks and downstream rearrangements. We identified the centromeres in C. auris and related species to study their involvement in the evolution and karyotype diversity reported in C. auris. We report conserved centromere features in 10 related species and trace the events that occurred at the centromeres during evolution. We reveal a centromere inactivation-mediated chromosome number change in these closely related species. We also observe that one of the geographical clades, the East Asian clade, evolved along a unique trajectory, compared to the other clades and related species.

The thermotolerant multidrug-resistant ascomycete Candida auris rapidly emerged since 2009 and simultaneously evolved in different geographical zones worldwide, causing superficial as well as systemic infections. The molecular events that orchestrated this sudden emergence of the killer fungus remain mostly elusive. Here, we identify centromeres in C. auris and related species, using a combined approach of chromatin immunoprecipitation and comparative genomic analyses. We find that C. auris and multiple other species in the Clavispora/Candida clade shared a conserved small regional centromere landscape lacking pericentromeres. Further, a centromere inactivation event led to karyotypic alterations in this species complex. Inter-species genome analysis identified several structural chromosomal changes around centromeres. In addition, centromeres are found to be rapidly evolving loci among the different geographical clades of the same species of C. auris. Finally, we reveal an evolutionary trajectory of the unique karyotype associated with clade 2 that consists of the drug susceptible isolates of C. auris.

There is strong interest globally amidst the current COVID-19 pandemic in tracing contacts of infectious patients using mobile technologies, both as a warning system to individuals and as a targeted intervention strategy for governments. Several governments, including India, have introduced mobile apps for this purpose, which give a warning when the individual’s phone establishes bluetooth contact with the phone of an infected person. We present a methodology to probabilistically evaluate risk of infection given the network of contacts that individuals are likely to encounter in real life. Instead of binary “infected” or “uninfected” statuses, an infection risk probability is maintained which can be efficiently calculated based on probabilities of recent contacts, and updated when a recent contact is diagnosed with a disease. We demonstrate on realistic networks that this method sharply outperforms a naive immediate-contact method even in an ideal circumstance that all infected persons are known to the naive method. We demonstrate robustness to missing contact information (such as when phones fail to make bluetooth contact or the app is not installed). We show, within our model, a strong flattening of the infectious peak when even a small fraction of cases are identified, tested and isolated. In the real world, where most known-infected persons are isolated or quarantined and where many individuals may not carry their mobiles in public, we believe the improvement offered by our method warrants consideration. Importantly, in view of widespread concerns on privacy and contact-tracing, our method relies mainly on direct contact data that can be stored locally on users’ phones, and uses limited communication via intermediary servers only upon testing, mitigating privacy concerns.

Genomic rearrangements associated with speciation often result in variation in chromosome number among closely related species. Malassezia species show variable karyotypes ranging between six and nine chromosomes. Here, we experimentally identified all eight centromeres in M. sympodialis as 3–5-kb long kinetochore-bound regions that span an AT-rich core and are depleted of the canonical histone H3. Centromeres of similar sequence features were identified as CENP-A-rich regions in Malassezia furfur, which has seven chromosomes, and histone H3 depleted regions in Malassezia slooffiae and Malassezia globosa with nine chromosomes each. Analysis of synteny conservation across centromeres with newly generated chromosome-level genome assemblies suggests two distinct mechanisms of chromosome number reduction from an inferred nine-chromosome ancestral state: (a) chromosome breakage followed by loss of centromere DNA and (b) centromere inactivation accompanied by changes in DNA sequence following chromosome–chromosome fusion. We propose that AT-rich centromeres drive karyotype diversity in the Malassezia species complex through breakage and inactivation.

Genomic rearrangements associated with speciation often result in variation in chromosome number among closely related species. Malassezia species show variable karyotypes ranging between six and nine chromosomes. Here, we experimentally identified all eight centromeres in M. sympodialis as 3–5-kb long kinetochore-bound regions that span an AT-rich core and are depleted of the canonical histone H3. Centromeres of similar sequence features were identified as CENP-A-rich regions in Malassezia furfur, which has seven chromosomes, and histone H3 depleted regions in Malassezia slooffiae and Malassezia globosa with nine chromosomes each. Analysis of synteny conservation across centromeres with newly generated chromosome-level genome assemblies suggests two distinct mechanisms of chromosome number reduction from an inferred nine-chromosome ancestral state: (a) chromosome breakage followed by loss of centromere DNA and (b) centromere inactivation accompanied by changes in DNA sequence following chromosome–chromosome fusion. We propose that AT-rich centromeres drive karyotype diversity in the Malassezia species complex through breakage and inactivation., eLife digest Millions of yeast, bacteria and other microbes live in or on the human body. A type of yeast known as Malassezia is one of the most abundantmicrobes living on our skin. Generally, Malassezia do not cause symptoms in humans but are associated with dandruff, dermatitis and other skin conditions in susceptible individuals. They have also been found in the human gut, where they exacerbate Crohn’s disease and pancreatic cancer. There are 18 closely related species of Malassezia and all have an unusually small amount of genetic material compared with other types of yeast. In yeast, like in humans, the genetic material is divided among several chromosomes. The number of chromosomes in different Malassezia species varies between six and nine. A region of each chromosome known as the centromere is responsible for ensuring that the equal numbers of chromosomes are passed on to their offspring. This means that any defects in centromeres can lead to the daughter yeast cells inheriting unequal numbers of chromosomes. Changes in chromosome number can drive the evolution of new species, but it remains unclear if and how centromere loss may have contributed to the evolution of Malassezia species. Sankaranarayanan et al. have now used biochemical, molecular genetic, and comparative genomic approaches to study the chromosomes of Malassezia species. The experiments revealed that nine Malassezia species had centromeres that shared common features such as being rich in adenine and thymine nucleotides, two of the building blocks of DNA. Sankaranarayanan et al. propose that these adenines and thymines make the centromeres more fragile leading to occasional breaks. This may have contributed to the loss of centromeres in some Malassezia cells and helped new species to evolve with fewer chromosomes. A better understanding of how Malassezia organize their genetic material should enable in-depth studies of how these yeasts interact with their human hosts and how they contribute to skin disease, cancer, Crohn’s disease and other health conditions. More broadly, these findings may help scientists to better understand how changes in chromosomes cause new species to evolve.

ChIP-seq (Chromatin Immunoprecipitation followed by sequencing) is a high-throughput technique to identify genomic regions that are bound in vivo by a particular protein, e.g., a transcription factor (TF). Biological factors, such as chromatin state, indirect and cooperative binding, as well as experimental factors, such as antibody quality, cross-linking, and PCR biases, are known to affect the outcome of ChIP-seq experiments. However, the relative impact of these factors on inferences made from ChIP-seq data is not entirely clear. Here, via a detailed ChIP-seq simulation pipeline, ChIPulate, we assess the impact of various biological and experimental sources of variation on several outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of replicates needed to confidently infer binding strength. We find that the TF motif can be recovered despite poor and non-uniform extraction and PCR amplification efficiencies. The recovery of the motif is, however, affected to a larger extent by the fraction of sites that are either cooperatively or indirectly bound. Importantly, our simulations reveal that the number of ChIP-seq replicates needed to accurately measure in vivo occupancy at high-affinity sites is larger than the recommended community standards. Our results establish statistical limits on the accuracy of inferences of protein-DNA binding from ChIP-seq and suggest that increasing the mean extraction efficiency, rather than amplification efficiency, would better improve sensitivity. The source code and instructions for running ChIPulate can be found at https://github.com/vishakad/chipulate., Author summary DNA-binding proteins perform many key roles in biology, such as transcriptional regulation of gene expression and chromatin modification. ChIP-seq (Chromatin immunoprecipitation followed by high-throughput sequencing) is a widely used experimental technique to identify DNA-binding sites of specific proteins of interest, within cells, genome-wide. DNA fragments from genomic regions that are bound by a protein of interest, often a transcription factor (TF), are selectively extracted using specific antibodies, amplified using PCR, and sequenced. The sequences are mapped to the reference genome. Regions where many sequences map, called “peaks”, are used to infer the location of TF-bound loci (peaks), in vivo occupancy at those loci, and the sequence pattern (motif) to which the TF shows a binding affinity. But measurements of TF occupancy and motif inference are vulnerable to several biological and experimental sources of variation that are poorly understood and difficult to assess directly. Here, we simulate key steps of the ChIP-seq protocol with the aim of estimating the relative effects of various sources of variations on motif inference and binding affinity estimations. Besides providing specific insights and recommendations, we provide a general framework to simulate sequence reads in a ChIP-seq experiment, which should considerably aid in the development of software aimed at analyzing ChIP-seq data.

Intra-chromosomal or inter-chromosomal genomic rearrangements often lead to speciation (1). Loss or gain of a centromere leads to alterations in chromosome number in closely related species. Thus, centromeres can enable tracing the path of evolution from the ancestral to a derived state (2). TheMalasseziaspecies complex of the phylum Basiodiomycota shows remarkable diversity in chromosome number ranging between six and nine chromosomes (3–5). To understand these transitions, we experimentally identified all eight centromeres as binding sites of an evolutionarily conserved outer kinetochore protein Mis12/Mtw1 inM. sympodialis. The 3 to 5 kb centromere regions share an AT-rich, poorly transcribed core region enriched with a 12 bp consensus motif. We also mapped nine such AT-rich centromeres inM. globosaand the related speciesMalassezia restrictaandMalassezia slooffiae. While eight predicted centromeres were found within conserved synteny blocks between these species andM. sympodialis, the remaining centromere inM. globosa(MgCEN2) or its orthologous centromere inM. slooffiae(MslCEN4) andM. restricta(MreCEN8) mapped to a synteny breakpoint compared withM. sympodialis. Taken together, we provide evidence that breakage and loss of a centromere (CEN2) in an ancestralMalasseziaspecies possessing nine chromosomes resulted in fewer chromosomes inM. sympodialis. Strikingly, the predicted centromeres of all closely relatedMalasseziaspecies map to an AT-rich core on each chromosome that also shows enrichment of the 12 bp sequence motif. We propose that centromeres are fragile AT-rich sites driving karyotype diversity through breakage and inactivation in these and other species.Significance statementThe number of chromosomes can vary between closely related species. Centromere loss destabilizes chromosomes and results in reduced number of chromosomes to drive speciation. A series of evidence from studies on various cancers suggest that an imbalance in kinetochore-microtubule attachments results in breaks at the centromeres. To understand if such events can cause chromosome number changes in nature, we studied six species ofMalassezia, of which three possess eight chromosomes and others have nine chromosomes each. We find signatures of chromosome breakage at the centromeres in organisms having nine chromosomes. We propose that the break at the centromere followed by fusions of acentric chromosomes to other chromosomes could be a plausible mechanism shaping the karyotype ofMalasseziaand related organisms.ClassificationBiological sciences, Genetics

Spatiotemporal regulation in DNA replication maintains kinetochore stability. The epigenetically regulated centromeres (CENs) in the budding yeastCandida albicanshave unique DNA sequences, replicate early and are clustered throughout the cell cycle. In this study, the genome-wide occupancy of replication initiation protein Orc4 reveals its abundance at all CENs inC. albicans. Orc4 associates with four different DNA motifs, one of which coincides with tRNA genes. Hi-C combined with genome-wide replication timing analyses identify enriched interactions among early or late replicating Orc4-bound regions. A simulated polymer model of chromosomes reveals that early replicating and strongly enriched Orc4-bound sites localize towards the kinetochores. Orc4 is constitutively localized to CENs, and both Orc4 and Mcm2 stabilize CENPA. CENPA chaperone Scm3 localizes at the kinetochore during anaphase, coinciding with the loading time of CENPA. We propose that this spatiotemporal nuclear localization of Orc4, with Mcm2 and Scm3, recruits CENPA and stabilizes centromeric chromatin.

Vasoplegia observed post cardiopulmonary bypass (CPB) is associated with substantial morbidity, multiple organ failure and mortality. Circulating counts of hematopoietic stem cells (HSCs) and endothelial progenitor cells (EPC) are potential markers of neo-vascularization and vascular repair. However, the significance of changes in the circulating levels of these progenitors in perioperative CPB, and their association with post-CPB vasoplegia, are currently unexplored. We enumerated HSC and EPC counts, via flow cytometry, at different time-points during CPB in 19 individuals who underwent elective cardiac surgery. These 19 individuals were categorized into two groups based on severity of post-operative vasoplegia, a clinically insignificant vasoplegic Group 1 (G1) and a clinically significant vasoplegic Group 2 (G2). Differential changes in progenitor cell counts during different stages of surgery were compared across these two groups. Machine-learning classifiers (logistic regression and gradient boosting) were employed to determine if differential changes in progenitor counts could aid the classification of individuals into these groups. Enumerating progenitor cells revealed an early and significant increase in the circulating counts of CD34+ and CD34+CD133+ hematopoietic stem cells (HSC) in G1 individuals, while these counts were attenuated in G2 individuals. Additionally, EPCs (CD34+VEGFR2+) were lower in G2 individuals compared to G1. Gradient boosting outperformed logistic regression in assessing the vasoplegia grouping based on the fold change in circulating CD 34+ levels. Our findings indicate that a lack of early response of CD34+ cells and CD34+CD133+ HSCs might serve as an early marker for development of clinically significant vasoplegia after CPB.

Transcription factors (TFs) often work cooperatively, where the binding of one TF to DNA enhances the binding affinity of a second TF to a nearby location. Such cooperative binding is important for activating gene expression from promoters and enhancers in both prokaryotic and eukaryotic cells. Existing methods to detect cooperative binding of a TF pair rely on analyzing the sequence that is bound. We propose a method that uses, instead, only ChIP-seq peak intensities and an expectation maximization (CPI-EM) algorithm. We validate our method using ChIP-seq data from cells where one of a pair of TFs under consideration has been genetically knocked out. Our algorithm relies on our observation that cooperative TF-TF binding is correlated with weak binding of one of the TFs, which we demonstrate in a variety of cell types, includingE. coli, S. cerevisiaeandM. musculuscells. We show that this method performs significantly better than a predictor based only on the ChIP-seq peak distance of the TFs under consideration. This suggests that peak intensities contain information that can help detect the cooperative binding of a TF pair. CPI-EM also outperforms an existing sequence-based algorithm in detecting cooperative binding. The CPI-EM algorithm is available athttps://github.com/vishakad/cpi-em.

A common function of the TFIID and SAGA complexes, which are recruited by transcriptional activators, is to deliver TBP to promoters to stimulate transcription. Neither the relative contributions of the five shared TBP-associated factor (TAF) subunits in TFIID and SAGA nor the requirement for different domains in shared TAFs for transcriptional activation is well understood. In this study, we uncovered the essential requirement for the highly conserved C-terminal region (CRD) of Taf9, a shared TAF, for transcriptional activation in yeast. Transcriptome profiling performed under Gcn4-activating conditions showed that the Taf9 CRD is required for induced expression of ∼9% of the yeast genome. The CRD was not essential for the Taf9-Taf6 interaction, TFIID or SAGA integrity, or Gcn4 interaction with SAGA in cell extracts. Microarray profiling of a SAGA mutant (spt20Δ) yielded a common set of genes induced by Spt20 and the Taf9 CRD. Chromatin immunoprecipitation (ChIP) assays showed that, although the Taf9 CRD mutation did not impair Gcn4 occupancy, the occupancies of TFIID, SAGA, and the preinitiation complex were severely impaired at several promoters. These results suggest a crucial role for the Taf9 CRD in genome-wide transcription and highlight the importance of conserved domains, other than histone fold domains, as a common determinant for TFIID and SAGA functions.

We present THiCweed, a new approach to analyzing transcription factor binding data from high-throughput chromatin-immunoprecipitation-sequencing (ChIP-seq) experiments. THiCweed clusters bound regions based on sequence similarity using a divisive hierarchical clustering approach based on sequence similarity within sliding windows, while exploring both strands. ThiCweed is specially geared towards data containing mixtures of motifs, which present a challenge to traditional motif-finders. Our implementation is significantly faster than standard motif-finding programs, able to process 30,000 peaks in 1-2 hours, on a single CPU core of a desktop computer. On synthetic data containing mixtures of motifs it is as accurate or more accurate than all other tested programs.THiCweed performs best with large “window” sizes (≥ 50bp), much longer than typical binding sites (7-15 base pairs). On real data it successfully recovers literature motifs, but also uncovers complex sequence characteristics in flanking DNA, variant motifs, and secondary motifs even when they occur in < 5% of the input, all of which appear biologically relevant. We also find recurring sequence patterns across diverse ChIP-seq data sets, possibly related to chromatin architecture and looping. THiCweed thus goes beyond traditional motif-finding to give new insights into genomic TF binding complexity.

The centromere, on which kinetochore proteins assemble, ensures precise chromosome segregation. Centromeres are largely specified by the histone H3 variant CENP-A (also known as Cse4 in yeasts). Structurally, centromere DNA sequences are highly diverse in nature. However, the evolutionary consequence of these structural diversities on de novo CENP-A chromatin formation remains elusive. Here, we report the identification of centromeres, as the binding sites of four evolutionarily conserved kinetochore proteins, in the human pathogenic budding yeast Candida tropicalis. Each of the seven centromeres comprises a 2 to 5 kb non-repetitive mid core flanked by 2 to 5 kb inverted repeats. The repeat-associated centromeres of C. tropicalis all share a high degree of sequence conservation with each other and are strikingly diverged from the unique and mostly non-repetitive centromeres of related Candida species—Candida albicans, Candida dubliniensis, and Candida lusitaniae. Using a plasmid-based assay, we further demonstrate that pericentric inverted repeats and the underlying DNA sequence provide a structural determinant in CENP-A recruitment in C. tropicalis, as opposed to epigenetically regulated CENP-A loading at centromeres in C. albicans. Thus, the centromere structure and its influence on de novo CENP-A recruitment has been significantly rewired in closely related Candida species. Strikingly, the centromere structural properties along with role of pericentric repeats in de novo CENP-A loading in C. tropicalis are more reminiscent to those of the distantly related fission yeast Schizosaccharomyces pombe. Taken together, we demonstrate, for the first time, fission yeast-like repeat-associated centromeres in an ascomycetous budding yeast., Author Summary Centromeres aid in high fidelity chromosome segregation. Paradoxically, centromere DNA sequences are rapidly evolving in fungi, plants, and animals. Centromere DNA sequences in fungi can be unique in each chromosome or share conserved features such as motifs for sequence specific protein binding, pericentric repeats, or transposon-rich elements. Ascomycetous fungi, in particular, show a wide range of diversity in centromere sequence elements. However, no ascomycetous budding yeast species is known to possess repeat-associated centromeres in all of its chromosomes. Here, we identified and mapped all seven centromeres in an ascomycete, a rapidly emerging human pathogenic yeast, Candida tropicalis. The repeat-associated centromeres of highly homogeneous DNA sequences in C. tropicalis are significantly diverged from the mostly non-repetitive unique centromeric DNA sequences of its closely related sequenced species, Candida albicans, Candida dubliniensis and Candida lusitaniae. Structurally, the centromeres of C. tropicalis more closely resemble those of the distantly related fission yeast Schizosaccharomyces pombe. Thus, we discover rapidly diverging repeat-associated centromeres in an ascomycetous budding yeast and provide evidence of emergence of repeat-associated centromeres via two independent evolutionary events in ascomycetous fungi.

We present THiCweed, a new approach to analyzing transcription factor binding data from high-throughput chromatin immunoprecipitation-sequencing (ChIP-seq) experiments. THiCweed clusters bound regions based on sequence similarity using a divisive hierarchical clustering approach based on sequence similarity within sliding windows, while exploring both strands. ThiCweed is specially geared toward data containing mixtures of motifs, which present a challenge to traditional motif-finders. Our implementation is significantly faster than standard motif-finding programs, able to process 30 000 peaks in 1–2 h, on a single CPU core of a desktop computer. On synthetic data containing mixtures of motifs it is as accurate or more accurate than all other tested programs. THiCweed performs best with large ‘window’ sizes (≥50 bp), much longer than typical binding sites (7–15 bp). On real data it successfully recovers literature motifs, but also uncovers complex sequence characteristics in flanking DNA, variant motifs and secondary motifs even when they occur in

The Cse4p-containing centromere regions of Candida albicans have unique and different DNA sequences on each of the eight chromosomes. In a closely related yeast, C. dubliniensis , we have identified the centromeric histone, CdCse4p, and shown that it is localized at the kinetochore. We have identified putative centromeric regions, orthologous to the C. albicans centromeres, in each of the eight C. dubliniensis chromosomes by bioinformatic analysis. Chromatin immunoprecipitation followed by PCR using a specific set of primers confirmed that these regions bind CdCse4p in vivo . As in C. albicans , the CdCse4p-associated core centromeric regions are 3–5 kb in length and show no sequence similarity to one another. Comparative sequence analysis suggests that the Cse4p-rich centromere DNA sequences in these two species have diverged faster than other orthologous intergenic regions and even faster than our best estimated “neutral” mutation rate. However, the location of the centromere and the relative position of Cse4p-rich centromeric chromatin in the orthologous regions with respect to adjacent ORFs are conserved in both species, suggesting that centromere identity is not solely determined by DNA sequence. Unlike known point and regional centromeres of other organisms, centromeres in C. albicans and C. dubliniensis have no common centromere-specific sequence motifs or repeats except some of the chromosome-specific pericentric repeats that are found to be similar in these two species. We propose that centromeres of these two Candida species are of an intermediate type between point and regional centromeres.

Genomewide techniques to assay gene expression and transcription factor binding are in widespread use, but are far from providing predictive rules for the function of regulatory DNA. To investigate more intensively the grammar rules for active regulatory sequence, we made libraries from random ligations of a very restricted set of sequences. Working with the yeast Saccharomyces cerevisiae, we developed a novel screen based on the sensitivity of ascospores lacking dityrosine to treatment with lytic enzymes. We tested two separate libraries built by random ligation of a single type of activator site either for a well-characterized sporulation factor, Ndt80, or for a new sporulation-specific regulatory site that we identified and several neutral spacer elements. This selective system achieved up to 1:104 enrichment of the artificial sequences that were active during sporulation, allowing a high-throughput analysis of large libraries of synthetic promoters. This is not practical with methods involving direct screening for expression, such as those based on fluorescent reporters. There were very few false positives, since active promoters always passed the screen when retested. The survival rate of our libraries containing roughly equal numbers of spacers and activators was a few percent that of libraries made from activators alone. The sequences of ∼100 examples of active and inactive promoters could not be distinguished by simple binary rules; instead, the best model for the data was a linear regression fit of a quantitative measure of gene activity to multiple features of the regulatory sequence.

A common function of the TFIID and SAGA complexes, which are recruited by transcriptional activators, is to deliver TBP to promoters to stimulate transcription. Neither the relative contributions of the five shared TBP-associated factor (TAF) subunits in TFIID and SAGA nor the requirement for different domains in shared TAFs for transcriptional activation is well understood. In this study, we uncovered the essential requirement for the highly conserved C-terminal region (CRD) of Taf9, a shared TAF, for transcriptional activation in yeast. Transcriptome profiling performed under Gcn4-activating conditions showed that the Taf9 CRD is required for induced expression of ∼9% of the yeast genome. The CRD was not essential for the Taf9-Taf6 interaction, TFIID or SAGA integrity, or Gcn4 interaction with SAGA in cell extracts. Microarray profiling of a SAGA mutant (spt20Δ) yielded a common set of genes induced by Spt20 and the Taf9 CRD. Chromatin immunoprecipitation (ChIP) assays showed that, although the Taf9 CRD mutation did not impair Gcn4 occupancy, the occupancies of TFIID, SAGA, and the preinitiation complex were severely impaired at several promoters. These results suggest a crucial role for the Taf9 CRD in genome-wide transcription and highlight the importance of conserved domains, other than histone fold domains, as a common determinant for TFIID and SAGA functions.

In the earlier part of this article, an introduction to musical scales, counterpoint and harmony was given. In this part these themes are developed further with reference to Bach’s music and the emergence of the equally tempered scale.

I couldn’t resist the choice of title, but in fact I am not supposing that the readers are dummies. I assume no prior knowledge of quantum mechanics, but a basic knowledge of vectors and of matrices would be useful. Readers who are unfamiliar with these can try ploughing ahead anyway, and they can look up a definition of matrix multiplication and play around with the examples when they get that far. The uncertainty principle of Heisenberg is one of the most famous statements in science this century, but it causes a lot of confusion among students and even among teachers. For instance, this periodical has been receiving requests to clarify issues like “Is the uncertainty principle compatible with Schrodinger’s equation?” “Where does the uncertainty principle come from?” and “Must any solution of Schrodinger’s equation obey the uncertainty principle?” In fact the uncertainty principle has nothing to do with Schrodinger’s equation. It comes from the basic assumptions of quantum mechanics: it must automatically be obeyed by any state, whether or not it obeys Schrodinger’s equation. In addition, of course, any state given at an instant as a function of spatial coordinates must evolve in time according to Schrodinger’s time dependent equation. The confusion perhaps stems from the usual statement of the uncertainty principle in terms of position and momentum, and one’s confusion with the classical versions of these objects which always have exact values. The student can’t see why they don’t (and cannot) have exact values in quantum mechanics (at least not simultaneously). The reason is that in quantum mechanics, these objects are not numbers, but “operators” which act on a state, and may either leave it unchanged but multiplied by a constant (so that they’re effectively like numbers) or change it entirely to something else (so that they’re not like numbers at all). Our classical idea of a position, or a momentum, is then some sort of averaging of the real effect of these operators. All this tends to be a bit too confusing for the beginning student so we’ll try to clarify it with the example of spins, which have their own angular-momentum uncertainty relations, and which are also not a comfortable topic for beginners. After hopefully making this a bit clear, we’ll move on to the case of position and momentum. We won’t actually derive the uncertainty principle, but merely make it plausible.

n51 N ] 2 ]un 2 1h( n51 N e 2~un112un! , ~1! where the un are displacements from equilibrium sites. We have chosen appropriate units to remove \, m ~the mass of the particle!, and the length scale of the potential. The infinite system also has a linear term in the potential ~to cancel the one in the exponential!, but with periodic boundary conditions this vanishes. h is a measure of the anharmonicity and also of the scale of the quantum effects. The larger h is, the more ‘‘classical’’ the system and the more harmonic the low-energy excitations. In the classical limit the parameter h can be scaled out but in the quantum case this can only be done by introducing an \fi1 in the above equation. We shall occasionally write

A key early player in the regulation of myoblast fusion is the gene dumbfounded (duf, also known as kirre). Duf must be expressed, and function, in founder cells (FCs). A fixed number of FCs are chosen from a pool of equivalent myoblasts and serve to attract fusion-competent myoblasts (FCMs) to fuse with them to form a multinucleate muscle-fibre. The spatial and temporal regulation of duf expression and function are important and play a deciding role in choice of fibre number, location and perhaps size. We have used a combination of bioinformatics and functional enhancer deletion approaches to understand the regulation of duf. By transgenic enhancer-reporter deletion analysis of the duf regulatory region, we found that several distinct enhancer modules regulate duf expression in specific muscle founders of the embryo and the adult. In addition to existing bioinformatics tools, we used a new program for analysis of regulatory sequence, PhyloGibbs-MP, whose development was largely motivated by the requirements of this work. The results complement our deletion analysis by identifying transcription factors whose predicted binding regions match with our deletion constructs. Experimental evidence for the relevance of some of these TF binding sites comes from available ChIP-on-chip from the literature, and from our analysis of localization of myogenic transcription factors with duf enhancer reporter gene expression. Our results demonstrate the complex regulation in each founder cell of a gene that is expressed in all founder cells. They provide evidence for transcriptional control—both activation and repression—as an important player in the regulation of myoblast fusion. The set of enhancer constructs generated will be valuable in identifying novel trans-acting factor-binding sites and chromatin regulation during myoblast fusion in Drosophila. Our results and the bioinformatics tools developed provide a basis for the study of the transcriptional regulation of other complex genes.

PhyloGibbs is a program that uses Gibbs sampling to predict putative binding sites for transcription factors in DNA. It has two notable advances over previous algorithms for this task: it handles phylogenetically related sequence systematically, and it evaluates the significance of each predicted site via statistical sampling. In this article, we explain how to use PhyloGibbs effectively. We describe the essential command-line options in detail, and discuss other considerations that arise in practical situations.

PhyloGibbs is a program that uses Gibbs sampling to predict putative binding sites for transcription factors in DNA. It has two notable advances over previous algorithms for this task: it handles phylogenetically related sequence systematically, and it evaluates the significance of each predicted site via statistical sampling. In this article, we explain how to use PhyloGibbs effectively. We describe the essential command-line options in detail, and discuss other considerations that arise in practical situations.