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The complex molecular networks employed in the execution of several complex biological processes are examined. The analysis helps in understanding the mechanisms involved in the normal cellular processes and disease pathologies, as these networks are different from the random networks.

A combination of strategies involving high-density pyrosequencing and insertional mutagenesis was used to explore the pathogenic content of Acinetobacter baumanni. The DNA sequencing approach allowed elucidating any sequence of microbe and when combined with genetic screen could identify many novel genes important for microbial pathogenesis.

An investigation of the transcriptional circuitry controlling pseudohyphal development in Saccharomyces cerevisiae to understand the organization of the transcriptional networks that govern cell differentiation is presented. Target hubs can serve as master regulators whose activity is sufficient for the induction of complex developmental responses and therefore represent important regulatory nodes in biological networks.

The regions of human chromosome 22 bound by Single Transducers and Activators of Transcription (STAT)1 and STAT2 in interferon-treated cells are identified using chromatin immunoprecipitation and DNA microarray analysis. Results demonstrate that the DNA-binding behavior of STAT1 homodimers is not conserved between the different interferon treatment conditions.

Research has been conducted on cell cycle regulation in Saccharomyces cerevisiae. The study of the transcription factors' gene targets implicated in G1/S control suggests that a regulatory cascade may control the events which surround cell cycle START.

Research has been conducted on the yeast mitotic spindle and cleavage plane. Results indicate that in budding yeast the spindle has to be positioned relative to the cleavage apparatus in order to capture the astral microtubules at the bud neck.

Research has been conducted on protein localization. The localization of protein in different eukaryotes has been investigated via the use of the proteome-scale analysis and the localization data for the proteins of previously unknown function are presented.

Nim1-related kinases in yeast coordinate the cell cycle with organization of the peripheral cytoskeleton. Hsl1 physically associates with the septin cytoskeleton in vivo and Hsl1 kinase activity depends on septin's functioning as it should. For the fist time it has been seen that cells monitor organization of the cytoskeleton and that a cell cycle checkpoint exists and responds to errors in the peripheral cytoskeleton. Hsl1, Gin4 and Kcc4 have homologs in higher eukaryotes. It appears that Swe1/Wee1 regulation by the kinase class in question is conserved to a high degree.

Functional analysis of the proteome is an essential part of genomic research and to facilitate different proteomic approaches, a Moveable ORF (MORF) library of 5854 yeast expression plasmide was constructed, each expressing a sequence-verified ORF as a C-terminal ORF fusion protein, under regulated control. Global analysis of glycosylated proteins identifies 109 new confirmed N-linked and 345 candidate glycoproteins, nearly doubling the known yeast glycome.

Research has been conducted on human cromosome 22. The authors report the construction of DNA microarray which represents the unique sequences of this chromosome, and the use of the microarray for measuring global-transcriptional activity in placental poly(A) (super)+ RNA.

Research has been conducted on gene coding for structural rDNA and proteins. The authors have investigated insertions in the open reading frame contained within transcribed antisense to the 25S rRNA gene in the nuclear rDNA repeat region, and have presented the details.

We present an integrative approach, SeqFold, that combines high-throughput RNA structure profiling data with computational prediction for genome-scale reconstruction of RNA secondary structures. SeqFold transforms experimental RNA structure information into a structure preference profile (SPP) and uses it to select stable RNA structure candidates representing the structure ensemble. Under a high-dimensional classification framework, SeqFold efficiently matches a given SPP to the most likely cluster of structures sampled from the Boltzmann-weighted ensemble. SeqFold is able to incorporate diverse types of RNA structure profiling data, including parallel analysis of RNA structure (PARS), selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPESeq), fragmentation sequencing (FragSeq) data generated by deep sequencing, and conventional SHAPE data. Using the known structures of a wide range of mRNAs and noncoding RNAs as benchmarks, we demonstrate that SeqFold outperforms or matches existing approaches in accuracy and is more robust to noise in experimental data. Application of SeqFold to reconstruct the secondary structures of the yeast transcriptome reveals the diverse impact of RNA secondary structure on gene regulation, including translation efficiency, transcription initiation, and protein-RNA interactions. SeqFold can be easily adapted to incorporate any new types of high-throughput RNA structure profiling data and is widely applicable to analyze RNA structures in any transcriptome. [ABSTRACT FROM AUTHOR]

Copy number variation (CNV) in the genome is a complex phenomenon, and not completely understood. We have developed a method, CNVnator, for CNV discovery and genotyping from read-depth (RD) analysis of personal genome sequencing. Our method is based on combining the established mean-shift approach with additional refinements (multiple-bandwidth partitioning and GC correction) to broaden the range of discovered CNVs. We calibrated CNVnator using the extensive validation performed by the 1000 Genomes Project. Because of this, we could use CNVnator for CNV discovery and genotyping in a population and characterization of atypical CNVs, such as de novo and multi-allelic events. Overall, for CNVs accessible by RD, CNVnator has high sensitivity (86%-96%), low false-discovery rate (3%-20%), high genotyping accuracy (93%-95%), and high resolution in breakpoint discovery (<200 bp in 90% of cases with high sequencing coverage). Furthermore, CNVnator is complementary in a straightforward way to split-read and read-pair approaches: It misses CNVs created by retrotransposable elements, but more than half of the validated CNVs that it identifies are not detected by split-read or read-pair. By genotyping CNVs in the CEPH, Yoruba, and Chinese-Japanese populations, we estimated that at least 11% of all CNV loci involve complex, multi-allelic events, a considerably higher estimate than reported earlier. Moreover, among these events, we observed cases with allele distribution strongly deviating from Hardy-Weinberg equilibrium, possibly implying selection on certain complex loci. Finally, by combining discovery and genotyping, we identified six potential de novo CNVs in two family trios. [ABSTRACT FROM AUTHOR]

Splicing remains an incompletely understood process. Recent findings suggest that chromatin structure participates in its regulation. Here, we analyze the RNA from subcellular fractions obtained through RNA-seq in the cell line K562. We show that in the human genome, splicing occurs predominantly during transcription. We introduce the coSI measure, based on RNA-seq reads mapping to exon junctions and borders, to assess the degree of splicing completion around internal exons. We show that, as expected, splicing is almost fully completed in cytosolic polyA+ RNA. In chromatinassociated RNA (which includes the RNA that is being transcribed), for 5.6% of exons, the removal of the surrounding introns is fully completed, compared with 0.3% of exons for which no intron-removal has occurred. The remaining exons exist as a mixture of spliced and fewer unspliced molecules, with a median coSI of 0.75. Thus, most RNAs undergo splicing while being transcribed: "co-transcriptional splicing." Consistent with co-transcriptional spliceosome assembly and splicing, we have found significant enrichment of spliceosomal snRNAs in chromatin-associated RNA compared with other cellular RNA fractions and other nonspliceosomal snRNAs. CoSI scores decrease along the gene, pointing to a "first transcribed, first spliced" rule, yet more downstream exons carry other characteristics, favoring rapid, co-transcriptional intron removal. Exons with low coSI values, that is, in the process of being spliced, are enriched with chromatin marks, consistent with a role for chromatin in splicing during transcription. For alternative exons and long noncoding RNAs, splicing tends to occur later, and the latter might remain unspliced in some cases. [ABSTRACT FROM AUTHOR]

Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) has become the dominant technique for mapping transcription factor (TF) binding regions genome-wide. We performed an integrative analysis centered around 457 ChIP-seq data sets on 119 human TFs generated by the ENCODE Consortium. We identified highly enriched sequence motifs in most data sets, revealing new motifs and validating known ones. The motif sites (TF binding sites) are highly conserved evolutionarily and show distinct footprints upon DNase I digestion. We frequently detected secondary motifs in addition to the canonical motifs of the TFs, indicating tethered binding and cobinding between multiple TFs. We observed significant position and orientation preferences between many cobinding TFs. Genes specifically expressed in a cell line are often associated with a greater occurrence of nearby TF binding in that cell line. We observed cell-line--specific secondary motifs that mediate the binding of the histone deacetylase HDAC2 and the enhancer-binding protein EP300. TF binding sites are located in GC-rich, nucleosome-depleted, and DNase I sensitive regions, flanked by wellpositioned nucleosomes, and many of these features show cell type specificity. The GC-richness may be beneficial for regulating TF binding because, when unoccupied by a TF, these regions are occupied by nucleosomes in vivo. We present the results of our analysis in a TF-centric web repository Factorbook (http://factorbook.org) and will continually update this repository as more ENCODE data are generated. [ABSTRACT FROM AUTHOR]

Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TFbinding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner. [ABSTRACT FROM AUTHOR]

PPARGC1A is a transcriptional coactivator that binds to and coactivates a variety of transcription factors (TFs) to regulate the expression of target genes. PPARGC1A plays a pivotal role in regulating energy metabolism and has been implicated in several human diseases, most notably type II diabetes. Previous studies have focused on the interplay between PPARGC1A and individual TFs, but little is known about how PPARGC1A combines with all of its partners across the genome to regulate transcriptional dynamics. In this study, we describe a core PPARGC1A transcriptional regulatory network operating in HepG2 cells treated with forskolin. We first mapped the genome-wide binding sites of PPARGC1A using chromatin-IP followed by high-throughput sequencing (ChIP-seq) and uncovered overrepresented DNA sequence motifs corresponding to known and novel PPARGC1A network partners. We then profiled six of these site-specific TF partners using ChIP-seq and examined their network connectivity and combinatorial binding patterns with PPARGC1A. Our analysis revealed extensive overlap of targets including a novel link between PPARGC1A and HSF1, a TF regulating the conserved heat shock response pathway that is misregulated in diabetes. Importantly, we found that different combinations of TFs bound to distinct functional sets of genes, thereby helping to reveal the combinatorial regulatory code for metabolic and other cellular processes. In addition, the different TFs often bound near the promoters and coding regions of each other's genes suggesting an intricate network of interdependent regulation. Overall, our study provides an important framework for understanding the systems-level control of metabolic gene expression in humans. [ABSTRACT FROM AUTHOR]

As the sequencing of healthy and disease genomes becomes more commonplace, detailed annotation provides interpretation for individual variation responsible for normal and disease phenotypes. Current approaches focus on direct changes in protein coding genes, particularly nonsynonymous mutations that directly affect the gene product. However, most individual variation occurs outside of genes and, indeed, most markers generated from genome-wide association studies (GWAS) identify variants outside of coding segments. Identification of potential regulatory changes that perturb these sites will lead to a better localization of truly functional variants and interpretation of their effects. We have developed a novel approach and database, RegulomeDB, which guides interpretation of regulatory variants in the human genome. RegulomeDB includes high-throughput, experimental data sets from ENCODE and other sources, as well as computational predictions and manual annotations to identify putative regulatory potential and identify functional variants. These data sources are combined into a powerful tool that scores variants to help separate functional variants from a large pool and provides a small set of putative sites with testable hypotheses as to their function. We demonstrate the applicability of this tool to the annotation of noncoding variants from 69 full sequenced genomes as well as that of a personal genome, where thousands of functionally associated variants were identified.Moreover, we demonstrate aGWAS where the database is able to quickly identify the known associated functional variant and provide a hypothesis as to its function. Overall, we expect this approach and resource to be valuable for the annotation of human genome sequences. [ABSTRACT FROM AUTHOR]

Genome-wide association studies have been successful in identifying single nucleotide polymorphisms (SNPs) associated with a large number of phenotypes. However, an associated SNP is likely part of a larger region of linkage disequilibrium. This makes it difficult to precisely identify the SNPs that have a biological link with the phenotype. We have systematically investigated the association of multiple types of ENCODE data with disease-associated SNPs and show that there is significant enrichment for functional SNPs among the currently identified associations. This enrichment is strongest when integrating multiple sources of functional information and when highest confidence disease-associated SNPs are used. We propose an approach that integrates multiple types of functional data generated by the ENCODE Consortium to help identify "functional SNPs" that may be associated with the disease phenotype. Our approach generates putative functional annotations for up to 80% of all previously reported associations. We show that for most associations, the functional SNP most strongly supported by experimental evidence is a SNP in linkage disequilibrium with the reported association rather than the reported SNP itself. Our results show that the experimental data sets generated by the ENCODE Consortium can be successfully used to suggest functional hypotheses for variants associated with diseases and other phenotypes. [ABSTRACT FROM AUTHOR]

Gene regulation at functional elements (e.g., enhancers, promoters, insulators) is governed by an interplay of nucleosome remodeling, histone modifications, and transcription factor binding. To enhance our understanding of gene regulation, the ENCODE Consortium has generated a wealth of ChIP-seq data on DNA-binding proteins and histone modifications. We additionally generated nucleosome positioning data on two cell lines, K562 and GM12878, by MNase digestion and high-depth sequencing. Here we relate 14 chromatin signals (12 histone marks, DNase, and nucleosome positioning) to the binding sites of 119 DNA-binding proteins across a large number of cell lines. We developed a new method for unsupervised pattern discovery, the Clustered AGgregation Tool (CAGT), which accounts for the inherent heterogeneity in signal magnitude, shape, and implicit strand orientation of chromatin marks. We applied CAGT on a total of 5084 data set pairs to obtain an exhaustive catalog of high-resolution patterns of histone modifications and nucleosome positioning signals around bound transcription factors. Our analyses reveal extensive heterogeneity in how histone modifications are deposited, and how nucleosomes are positioned around binding sites. With the exception of the CTCF/cohesin complex, asymmetry of nucleosome positioning is predominant. Asymmetry of histone modifications is also widespread, for all types of chromatin marks examined, including promoter, enhancer, elongation, and repressive marks. The fine-resolution signal shapes discovered by CAGT unveiled novel correlation patterns between chromatin marks, nucleosome positioning, and sequence content. Meta-analyses of the signal profiles revealed a common vocabulary of chromatin signals shared across multiple cell lines and binding proteins. [ABSTRACT FROM AUTHOR]

Signaling through mitogen-activated protein kinases (MPKs) cascades is a complex and fundamental process in eukaryotes, requiring MPK-activating kinases (MKKs) and MKK-activating kinases (MKKKs). However, to date only a limited number of MKK??"MPK interactions and MPK phosphorylation substrates have been revealed. We determined which Arabidopsis thaliana MKKs preferentially activate 10 different MPKs in vivo and used the activated MPKs to probe high-density protein microarrays to determine their phosphorylation targets. Our analyses revealed known and novel signaling modules encompassing 570 MPK phosphorylation substrates; these substrates were enriched in transcription factors involved in the regulation of development, defense, and stress responses. Selected MPK substrates were validated by in planta reconstitution experiments. A subset of activated and wild-type MKKs induced cell death, indicating a possible role for these MKKs in the regulation of cell death. Interestingly, MKK7- and MKK9-induced death requires Sgt1, a known regulator of cell death induced during plant innate immunity. Our predicted MKK??"MPK phosphorylation network constitutes a valuable resource to understand the function and specificity of MPK signaling systems. [ABSTRACT FROM AUTHOR]

Oocyte maturation, fertilization, and early embryonic development occur in the absence of gene transcription. Therefore, it is critical to understand at a global level the post-transcriptional events that are driving these transitions. Here we used a systems approach by combining polysome mRNA profiling and bioinformatics to identify RNA-binding motifs in mRNAs that either enter or exit the polysome pool during mouse oocyte maturation. Association of mRNA with the polysomes correlates with active translation. Using this strategy, we identified highly specific patterns of mRNA recruitment to the polysomes that are synchronized with the cell cycle. A large number of the mRNAs recovered with translating ribosomes contain motifs for the RNA-binding proteins DAZL (deleted in azoospermia-like) and CPEB (cytoplasmic polyadenylation element-binding protein). Although a Dazl role in early germ cell development is well established, no function has been described during oocyte-to-embryo transition. We demonstrate that CPEB1 regulates Dazl post-transcriptionally, and that DAZL is essential for meiotic maturation and embryonic cleavage. In the absence of DAZL synthesis, the meiotic spindle fails to form due to disorganization of meiotic microtubules. Therefore, Cpeb1 and Dazl function in a progressive, self-reinforcing pathway to promote oocyte maturation and early embryonic development. [ABSTRACT FROM AUTHOR]

We present an integrative machine learning method, incRNA, for whole-genome identification of noncoding RNAs (ncRNAs). It combines a large amount of expression data, RNA secondary-structure stability, and evolutionary conservation at the protein and nucleic-acid level. Using the incRNA model and data from the modENCODE consortium, we are able to separate known C. elegans ncRNAs from coding sequences and other genomic elements with a high level of accuracy (97% AUC on an independent validation set), and find more than 7000 novel ncRNA candidates, among which more than 1000 are located in the intergenic regions of C. elegans genome. Based on the validation set, we estimate that 91% of the approximately 7000 novel ncRNA candidates are true positives. We then analyze 15 novel ncRNA candidates by RT-PCR, detecting the expression for 14. In addition, we characterize the properties of all the novel ncRNA candidates and find that they have distinct expression patterns across developmental stages and tend to use novel RNA structural families. We also find that they are often targeted by specific transcription factors (?1/459% of intergenic novel ncRNA candidates). Overall, our study identifies many new potential ncRNAs in C. elegans and provides a method that can be adapted to other organisms. [ABSTRACT FROM AUTHOR]

Regulation of gene expression by sequence-specific transcription factors is central to developmental programs and depends on the binding of transcription factors with target sites in the genome. To date, most such analyses in Caenorhabditis elegans have focused on the interactions between a single transcription factor with one or a few select target genes. As part of the modENCODE Consortium, we have used chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq) to determine the genome-wide binding sites of 22 transcription factors (ALR-1, BLMP-1, CEH-14, CEH-30, EGL-27, EGL-5, ELT-3, EOR-1, GEI-11, HLH-1, LIN-11, LIN-13, LIN-15B, LIN-39, MAB-5, MDL-1, MEP-1, PES-1, PHA-4, PQM-1, SKN-1, and UNC-130) at diverse developmental stages. For each factor we determined candidate gene targets, both coding and non-coding. The typical binding sites of almost all factors are within a few hundred nucleotides of the transcript start site. Most factors target a mixture of coding and non-coding target genes, although one factor preferentially binds to non-coding RNA genes. We built a regulatory network among the 22 factors to determine their functional relationships to each other and found that some factors appear to act preferentially as regulators and others as target genes. Examination of the binding targets of three related HOX factors?"LIN-39, MAB-5, and EGL-5?"indicates that these factors regulate genes involved in cellular migration, neuronal function, and vulval differentiation, consistent with their known roles in these developmental processes. Ultimately, the comprehensive mapping of transcription factor binding sites will identify features of transcriptional networks that regulate C. elegans developmental processes. [ABSTRACT FROM AUTHOR]

Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning-based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution and then explicitly encapsulates gene-gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared with a wide range of conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases., (© 2025 Li et al.; Published by Cold Spring Harbor Laboratory Press.)

We developed an analysis pipeline that can extract microbial sequences from spatial transcriptomic (ST) data and assign taxonomic labels, generating a spatial microbial abundance matrix in addition to the default host expression matrix, enabling simultaneous analysis of host expression and microbial distribution. We called the pipeline spatial metatranscriptome (SMT) and applied it on both human and murine intestinal sections and validated the spatial microbial abundance information with alternative assays. Biological insights were gained from these novel data that showed host-microbe interaction at various spatial scales. Finally, we tested experimental modification that can increase microbial capture while preserving host spatial expression quality and, by use of positive controls, quantitatively showed the capture efficiency and recall of our methods. This proof-of-concept work shows the feasibility of SMT analysis and paves the way for further experimental optimization and application., (© 2023 Lyu et al.; Published by Cold Spring Harbor Laboratory Press.)

Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses., (© 2022 Gao et al.; Published by Cold Spring Harbor Laboratory Press.)