Reconstructing and reprogramming the tumor-propagating potential of glioblastoma stem-like cells.

Developmental fate decisions are dictated by master transcription factors (TFs) that interact with cis-regulatory elements to direct transcriptional programs. Certain malignant tumors may also depend on cellular hierarchies reminiscent of normal development but superimposed on underlying genetic aberrations. In glioblastoma (GBM), a subset of stem-like tumor-propagating cells (TPCs) appears to drive tumor progression and underlie therapeutic resistance yet remain poorly understood. Here, we identify a core set of neurodevelopmental TFs (POU3F2, SOX2, SALL2, and OLIG2) essential for GBM propagation. These TFs coordinately bind and activate TPC-specific regulatory elements and are sufficient to fully reprogram differentiated GBM cells to "induced" TPCs, recapitulating the epigenetic landscape and phenotype of native TPCs. We reconstruct a network model that highlights critical interactions and identifies candidate therapeutic targets for eliminating TPCs. Our study establishes the epigenetic basis of a developmental hierarchy in GBM, provides detailed insight into underlying gene regulatory programs, and suggests attendant therapeutic strategies. PAPERCLIP:

We need to keep a reproducible trace of facts, predictions, and hypotheses from gene to function in the era of big data.

How do we scale biological science to the demand of next generation biology and medicine to keep track of the facts, predictions, and hypotheses? These days, enormous amounts of DNA sequence and other omics data are generated. Since these data contain the blueprint for life, it is imperative that we interpret it accurately. The abundance of DNA is only one part of the challenge. Artificial Intelligence (AI) and network methods routinely build on large screens, single cell technologies, proteomics, and other modalities to infer or predict biological functions and phenotypes associated with proteins, pathways, and organisms. As a first step, how do we systematically trace the provenance of knowledge from experimental ground truth to gene function predictions and annotations? Here, we review the main challenges in tracking the evolution of biological knowledge and propose several specific solutions to provenance and computational tracing of evidence in functional linkage networks.

One for all and all for One: Improving replication of genetic studies through network diffusion.

Improving accuracy in genetic studies would greatly accelerate understanding the genetic basis of complex diseases. One approach to achieve such an improvement for risk variants identified by the genome wide association study (GWAS) approach is to incorporate previously known biology when screening variants across the genome. We developed a simple approach for improving the prioritization of candidate disease genes that incorporates a network diffusion of scores from known disease genes using a protein network and a novel integration with GWAS risk scores, and tested this approach on a large Alzheimer disease (AD) GWAS dataset. Using a statistical bootstrap approach, we cross-validated the method and for the first time showed that a network approach improves the expected replication rates in GWAS studies. Several novel AD genes were predicted including CR2, SHARPIN, and PTPN2. Our re-prioritized results are enriched for established known AD-associated biological pathways including inflammation, immune response, and metabolism, whereas standard non-prioritized results were not. Our findings support a strategy of considering network information when investigating genetic risk factors.

COMBREX-DB: an experiment centered database of protein function: knowledge, predictions and knowledge gaps.

The COMBREX database (COMBREX-DB; combrex.bu.edu) is an online repository of information related to (i) experimentally determined protein function, (ii) predicted protein function, (iii) relationships among proteins of unknown function and various types of experimental data, including molecular function, protein structure, and associated phenotypes. The database was created as part of the novel COMBREX (COMputational BRidges to EXperiments) effort aimed at accelerating the rate of gene function validation. It currently holds information on ∼ 3.3 million known and predicted proteins from over 1000 completely sequenced bacterial and archaeal genomes. The database also contains a prototype recommendation system for helping users identify those proteins whose experimental determination of function would be most informative for predicting function for other proteins within protein families. The emphasis on documenting experimental evidence for function predictions, and the prioritization of uncharacterized proteins for experimental testing distinguish COMBREX from other publicly available microbial genomics resources. This article describes updates to COMBREX-DB since an initial description in the 2011 NAR Database Issue.

Hypermutable DNA chronicles the evolution of human colon cancer.

Intratumor genetic heterogeneity reflects the evolutionary history of a cancer and is thought to influence treatment outcomes. Here we report that a simple PCR-based assay interrogating somatic variation in hypermutable polyguanine (poly-G) repeats can provide a rapid and reliable assessment of mitotic history and clonal architecture in human cancer. We use poly-G repeat genotyping to study the evolution of colon carcinoma. In a cohort of 22 patients, we detect poly-G variants in 91% of tumors. Patient age is positively correlated with somatic mutation frequency, suggesting that some poly-G variants accumulate before the onset of carcinogenesis during normal division in colonic stem cells. Poorly differentiated tumors have fewer mutations than well-differentiated tumors, possibly indicating a shorter mitotic history of the founder cell in these cancers. We generate poly-G mutation profiles of spatially separated samples from primary carcinomas and matched metastases to build well-supported phylogenetic trees that illuminate individual patients' path of metastatic progression. Our results show varying degrees of intratumor heterogeneity among patients. Finally, we show that poly-G mutations can be found in other cancers than colon carcinoma. Our approach can generate reliable maps of intratumor heterogeneity in large numbers of patients with minimal time and cost expenditure.

Identification of rare germline copy number variations over-represented in five human cancer types.

BACKGROUND: Copy number variations (CNVs) are increasingly recognized as significant disease susceptibility markers in many complex disorders including cancer. The availability of a large number of chromosomal copy number profiles in both malignant and normal tissues in cancer patients presents an opportunity to characterize not only somatic alterations but also germline CNVs, which may confer increased risk for cancer. RESULTS: We explored the germline CNVs in five cancer cohorts from the Cancer Genome Atlas (TCGA) consisting of 351 brain, 336 breast, 342 colorectal, 370 renal, and 314 ovarian cancers, genotyped on Affymetrix SNP6.0 arrays. Comparing these to ~3000 normal controls from another study, our case-control association study revealed 39 genomic loci (9 brain, 3 breast, 4 colorectal, 11 renal, and 12 ovarian cancers) as potential candidates of tumor susceptibility loci. Many of these loci are new and in some cases are associated with a substantial increase in disease risk. The majority of the observed loci do not overlap with coding sequences; however, several observed genomic loci overlap with known cancer genes including RET in brain cancers, ERBB2 in renal cell carcinomas, and DCC in ovarian cancers, all of which have not been previously associated with germline changes in cancer. CONCLUSIONS: This large-scale genome-wide association study for CNVs across multiple cancer types identified several novel rare germline CNVs as cancer predisposing genomic loci. These loci can potentially serve as clinically useful markers conferring increased cancer risk.

Systems biology characterization of engineered tissues.

Tissue engineering and molecular systems biology are inherently interdisciplinary fields that have been developed independently so far. In this review, we first provide a brief introduction to tissue engineering and to molecular systems biology. Next, we highlight some prominent applications of systems biology techniques in tissue engineering. Finally, we outline research directions that can successfully blend these two fields. Through these examples, we propose that experimental and computational advances in molecular systems biology can lead to predictive models of bioengineered tissues that enhance our understanding of bioengineered systems. In turn, the unique challenges posed by tissue engineering will usher in new experimental techniques and computational advances in systems biology.

COMBREX: a project to accelerate the functional annotation of prokaryotic genomes.

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector.

Here, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA sequencing (RNA-seq) and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico and helps characterize numerous understudied fusions in tumor and normal tissue samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and facilitates transparent explanation and interpretation of machine-learning predictions and their experimental sources.

Functional characterization of the YmcB and YqeV tRNA methylthiotransferases of Bacillus subtilis.

Methylthiotransferases (MTTases) are a closely related family of proteins that perform both radical-S-adenosylmethionine (SAM) mediated sulfur insertion and SAM-dependent methylation to modify nucleic acid or protein targets with a methyl thioether group (-SCH(3)). Members of two of the four known subgroups of MTTases have been characterized, typified by MiaB, which modifies N(6)-isopentenyladenosine (i(6)A) to 2-methylthio-N(6)-isopentenyladenosine (ms(2)i(6)A) in tRNA, and RimO, which modifies a specific aspartate residue in ribosomal protein S12. In this work, we have characterized the two MTTases encoded by Bacillus subtilis 168 and find that, consistent with bioinformatic predictions, ymcB is required for ms(2)i(6)A formation (MiaB activity), and yqeV is required for modification of N(6)-threonylcarbamoyladenosine (t(6)A) to 2-methylthio-N(6)-threonylcarbamoyladenosine (ms(2)t(6)A) in tRNA. The enzyme responsible for the latter activity belongs to a third MTTase subgroup, no member of which has previously been characterized. We performed domain-swapping experiments between YmcB and YqeV to narrow down the protein domain(s) responsible for distinguishing i(6)A from t(6)A and found that the C-terminal TRAM domain, putatively involved with RNA binding, is likely not involved with this discrimination. Finally, we performed a computational analysis to identify candidate residues outside the TRAM domain that may be involved with substrate recognition. These residues represent interesting targets for further analysis.

Triplet repeat length bias and variation in the human transcriptome.

Length variation in short tandem repeats (STRs) is an important family of DNA polymorphisms with numerous applications in genetics, medicine, forensics, and evolutionary analysis. Several major diseases have been associated with length variation of trinucleotide (triplet) repeats including Huntington's disease, hereditary ataxias and spinobulbar muscular atrophy. Using the reference human genome, we have catalogued all triplet repeats in genic regions. This data revealed a bias in noncoding DNA repeat lengths. It also enabled a survey of repeat-length polymorphisms (RLPs) in human genomes and a comparison of the rate of polymorphism in humans versus divergence from chimpanzee. For short repeats, this analysis of three human genomes reveals a relatively low RLP rate in exons and, somewhat surprisingly, in introns. All short RLPs observed in multiple genomes are biallelic (at least in this small sample). In contrast, long repeats are highly polymorphic and some long RLPs are multiallelic. For long repeats, the chimpanzee sequence frequently differs from all observed human alleles. This suggests a high expansion/contraction rate in all long repeats. Expansions and contractions are not, however, affected by natural selection discernable from our comparison of human-chimpanzee divergence with human RLPs. Our catalog of human triplet repeats and their surrounding flanking regions can be used to produce a cost-effective whole-genome assay to test individuals. This repeat assay could someday complement SNP arrays for producing tests that assess the risk of an individual to develop a disease, or become part of personalized genomic strategy that provides therapeutic guidance with respect to drug response.

RimO, a MiaB-like enzyme, methylthiolates the universally conserved Asp88 residue of ribosomal protein S12 in Escherichia coli.

Ribosomal protein S12 undergoes a unique posttranslational modification, methylthiolation of residue D88, in Escherichia coli and several other bacteria. Using mass spectrometry, we have identified the enzyme responsible for this modification in E. coli, the yliG gene product. This enzyme, which we propose be called RimO, is a radical-S-adenosylmethionine protein that bears strong sequence similarity to MiaB, which methylthiolates tRNA. We show that RimO and MiaB represent two of four subgroups of a larger, ancient family of likely methylthiotransferases, the other two of which are typified by Bacillus subtilis YqeV and Methanococcus jannaschii Mj0867, and we predict that RimO is unique among these subgroups in its modification of protein as opposed to tRNA. Despite this, RimO has not significantly diverged from the other three subgroups at the sequence level even within the C-terminal TRAM domain, which in the methyltransferase RumA is known to bind the RNA substrate and which we presume to be responsible for substrate binding and recognition in all four subgroups of methylthiotransferases. To our knowledge, RimO and MiaB represent the most extreme known case of resemblance between enzymes modifying protein and nucleic acid. The initial results presented here constitute a bioinformatics-driven prediction with preliminary experimental validation that should serve as the starting point for several interesting lines of further inquiry.

Genomewide analysis of PRC1 and PRC2 occupancy identifies two classes of bivalent domains.

In embryonic stem (ES) cells, bivalent chromatin domains with overlapping repressive (H3 lysine 27 tri-methylation) and activating (H3 lysine 4 tri-methylation) histone modifications mark the promoters of more than 2,000 genes. To gain insight into the structure and function of bivalent domains, we mapped key histone modifications and subunits of Polycomb-repressive complexes 1 and 2 (PRC1 and PRC2) genomewide in human and mouse ES cells by chromatin immunoprecipitation, followed by ultra high-throughput sequencing. We find that bivalent domains can be segregated into two classes -- the first occupied by both PRC2 and PRC1 (PRC1-positive) and the second specifically bound by PRC2 (PRC2-only). PRC1-positive bivalent domains appear functionally distinct as they more efficiently retain lysine 27 tri-methylation upon differentiation, show stringent conservation of chromatin state, and associate with an overwhelming number of developmental regulator gene promoters. We also used computational genomics to search for sequence determinants of Polycomb binding. This analysis revealed that the genomewide locations of PRC2 and PRC1 can be largely predicted from the locations, sizes, and underlying motif contents of CpG islands. We propose that large CpG islands depleted of activating motifs confer epigenetic memory by recruiting the full repertoire of Polycomb complexes in pluripotent cells.

Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2.

BACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. RESULTS: We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. CONCLUSIONS: We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.

High-throughput mediation analysis of human proteome and metabolome identifies mediators of post-bariatric surgical diabetes control.

To improve the power of mediation in high-throughput studies, here we introduce High-throughput mediation analysis (Hitman), which accounts for direction of mediation and applies empirical Bayesian linear modeling. We apply Hitman in a retrospective, exploratory analysis of the SLIMM-T2D clinical trial in which participants with type 2 diabetes were randomized to Roux-en-Y gastric bypass (RYGB) or nonsurgical diabetes/weight management, and fasting plasma proteome and metabolome were assayed up to 3 years. RYGB caused greater improvement in HbA1c, which was mediated by growth hormone receptor (GHR). GHR's mediation is more significant than clinical mediators, including BMI. GHR decreases at 3 months postoperatively alongside increased insulin-like growth factor binding proteins IGFBP1/BP2; plasma GH increased at 1 year. Experimental validation indicates (1) hepatic GHR expression decreases in post-bariatric rats; (2) GHR knockdown in primary hepatocytes decreases gluconeogenic gene expression and glucose production. Thus, RYGB may induce resistance to diabetogenic effects of GH signaling.Trial Registration: Clinicaltrials.gov NCT01073020.

Seeing the forest for the trees: using the Gene Ontology to restructure hierarchical clustering.

MOTIVATION: There is a growing interest in improving the cluster analysis of expression data by incorporating into it prior knowledge, such as the Gene Ontology (GO) annotations of genes, in order to improve the biological relevance of the clusters that are subjected to subsequent scrutiny. The structure of the GO is another source of background knowledge that can be exploited through the use of semantic similarity. RESULTS: We propose here a novel algorithm that integrates semantic similarities (derived from the ontology structure) into the procedure of deriving clusters from the dendrogram constructed during expression-based hierarchical clustering. Our approach can handle the multiple annotations, from different levels of the GO hierarchy, which most genes have. Moreover, it treats annotated and unannotated genes in a uniform manner. Consequently, the clusters obtained by our algorithm are characterized by significantly enriched annotations. In both cross-validation tests and when using an external index such as protein-protein interactions, our algorithm performs better than previous approaches. When applied to human cancer expression data, our algorithm identifies, among others, clusters of genes related to immune response and glucose metabolism. These clusters are also supported by protein-protein interaction data.

Integration of heterogeneous expression data sets extends the role of the retinol pathway in diabetes and insulin resistance.

MOTIVATION: Type 2 diabetes is a chronic metabolic disease that involves both environmental and genetic factors. To understand the genetics of type 2 diabetes and insulin resistance, the DIabetes Genome Anatomy Project (DGAP) was launched to profile gene expression in a variety of related animal models and human subjects. We asked whether these heterogeneous models can be integrated to provide consistent and robust biological insights into the biology of insulin resistance. RESULTS: We perform integrative analysis of the 16 DGAP data sets that span multiple tissues, conditions, array types, laboratories, species, genetic backgrounds and study designs. For each data set, we identify differentially expressed genes compared with control. Then, for the combined data, we rank genes according to the frequency with which they were found to be statistically significant across data sets. This analysis reveals RetSat as a widely shared component of mechanisms involved in insulin resistance and sensitivity and adds to the growing importance of the retinol pathway in diabetes, adipogenesis and insulin resistance. Top candidates obtained from our analysis have been confirmed in recent laboratory studies.

Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles.

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data.

Network-based analysis of affected biological processes in type 2 diabetes models.

Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein-protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein-protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder.

Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease.

BACKGROUND: Identifying and understanding the functional role of genetic risk factors for Alzheimer disease (AD) has been complicated by the variability of genetic influences across brain regions and confounding with age-related neurodegeneration. METHODS: A gene co-expression network was constructed using data obtained from the Allen Brain Atlas for multiple brain regions (cerebral cortex, cerebellum, and brain stem) in six individuals. Gene network analyses were seeded with 52 reproducible (i.e., established) AD (RAD) genes. Genome-wide association study summary data were integrated with the gene co-expression results and phenotypic information (i.e., memory and aging-related outcomes) from gene knockout studies in Drosophila to generate rankings for other genes that may have a role in AD. RESULTS: We found that co-expression of the RAD genes is strongest in the cortical regions where neurodegeneration due to AD is most severe. There was significant evidence for two novel AD-related genes including EPS8 (FDR p = 8.77 × 10-3) and HSPA2 (FDR p = 0.245). CONCLUSIONS: Our findings indicate that AD-related risk factors are potentially associated with brain region-specific effects on gene expression that can be detected using a gene network approach.