Definitive demonstration by synthesis of genome annotation completeness.

We develop a method for completing the genetics of natural living systems by which the absence of expected future discoveries can be established. We demonstrate the method using bacteriophage øX174, the first DNA genome to be sequenced. Like many well-studied natural organisms, closely related genome sequences are available-23 Bullavirinae genomes related to øX174. Using bioinformatic tools, we first identified 315 potential open reading frames (ORFs) within the genome, including the 11 established essential genes and 82 highly conserved ORFs that have no known gene products or assigned functions. Using genome-scale design and synthesis, we made a mutant genome in which all 11 essential genes are simultaneously disrupted, leaving intact only the 82 conserved but cryptic ORFs. The resulting genome is not viable. Cell-free gene expression followed by mass spectrometry revealed only a single peptide expressed from both the cryptic ORF and wild-type genomes, suggesting a potential new gene. A second synthetic genome in which 71 conserved cryptic ORFs were simultaneously disrupted is viable but with ∼50% reduced fitness relative to the wild type. However, rather than finding any new genes, repeated evolutionary adaptation revealed a single point mutation that modulates expression of gene H, a known essential gene, and fully suppresses the fitness defect. Taken together, we conclude that the annotation of currently functional ORFs for the øX174 genome is formally complete. More broadly, we show that sequencing and bioinformatics followed by synthesis-enabled reverse genomics, proteomics, and evolutionary adaptation can definitely establish the sufficiency and completeness of natural genome annotations.

Whole-genome sequence of Mucor velutinosus NIH1002, a strain associated with disseminated disease.

The draft genome of Mucor velutinosus NIH1002, a 2011 isolate from a case of disseminated disease, was sequenced using PacBio long-read and HiSeq short-read technologies. The genome has 43 contigs, an N50 of 2.65 Mb, and 13,295 protein-coding genes. It is the most complete M. velutinosus genome to date.

A lipid-associated macrophage lineage rewires the spatial landscape of adipose tissue in early obesity.

Adipose tissue macrophage (ATM) infiltration is associated with adipose tissue dysfunction and insulin resistance in mice and humans. Recent single-cell data highlight increased ATM heterogeneity in obesity but do not provide a spatial context for ATM phenotype dynamics. We integrated single-cell RNA-Seq, spatial transcriptomics, and imaging of murine adipose tissue in a time course study of diet-induced obesity. Overall, proinflammatory immune cells were predominant in early obesity, whereas nonresident antiinflammatory ATMs predominated in chronic obesity. A subset of these antiinflammatory ATMs were transcriptomically intermediate between monocytes and mature lipid-associated macrophages (LAMs) and were consistent with a LAM precursor (pre-LAM). Pre-LAMs were spatially associated with early obesity crown-like structures (CLSs), which indicate adipose tissue dysfunction. Spatial data showed colocalization of ligand-receptor transcripts related to lipid signaling among monocytes, pre-LAMs, and LAMs, including Apoe, Lrp1, Lpl, and App. Pre-LAM expression of these ligands in early obesity suggested signaling to LAMs in the CLS microenvironment. Our results refine understanding of ATM diversity and provide insight into the dynamics of the LAM lineage during development of metabolic disease.

OptiFit: an Improved Method for Fitting Amplicon Sequences to Existing OTUs.

Assigning amplicon sequences to operational taxonomic units (OTUs) is an important step in characterizing microbial communities across large data sets. A notable difference between de novo clustering and database-dependent reference clustering methods is that OTU assignments from de novo methods may change when new sequences are added. However, one may wish to incorporate new samples to previously clustered data sets without clustering all sequences again, such as when comparing across data sets or deploying machine learning models. Existing reference-based methods produce consistent OTUs but only consider the similarity of each query sequence to a single reference sequence in an OTU, resulting in assignments that are worse than those generated by de novo methods. To provide an efficient method to fit sequences to existing OTUs, we developed the OptiFit algorithm. Inspired by the de novo OptiClust algorithm, OptiFit considers the similarity of all pairs of reference and query sequences to produce OTUs of the best possible quality. We tested OptiFit using four data sets with two strategies: (i) clustering to a reference database and (ii) splitting the data set into a reference and query set, clustering the references using OptiClust, and then clustering the queries to the references. The result is an improved implementation of reference-based clustering. OptiFit produces OTUs of a quality similar to that of OptiClust at faster speeds when using the split data set strategy. OptiFit provides a suitable option for users requiring consistent OTU assignments at the same quality as afforded by de novo clustering methods. IMPORTANCE Advancements in DNA sequencing technology have allowed researchers to affordably generate millions of sequence reads from microorganisms in diverse environments. Efficient and robust software tools are needed to assign microbial sequences into taxonomic groups for characterization and comparison of communities. The OptiClust algorithm produces high-quality groups by comparing sequences to each other, but the assignments can change when new sequences are added to a data set, making it difficult to compare different studies. Other approaches assign sequences to groups by comparing them to sequences in a reference database to produce consistent assignments, but the quality of the groups produced is reduced compared to that with OptiClust. We developed OptiFit, a new reference-based algorithm that produces consistent yet high-quality assignments like OptiClust. OptiFit allows researchers to compare microbial communities across different studies or add new data to existing studies without sacrificing the quality of the group assignments.

Deciphering multi-way interactions in the human genome.

Chromatin architecture, a key regulator of gene expression, can be inferred using chromatin contact data from chromosome conformation capture, or Hi-C. However, classical Hi-C does not preserve multi-way contacts. Here we use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organization in the human genome. We use hypergraph theory for data representation and analysis, and quantify higher order structures in neonatal fibroblasts, biopsied adult fibroblasts, and B lymphocytes. By integrating multi-way contacts with chromatin accessibility, gene expression, and transcription factor binding, we introduce a data-driven method to identify cell type-specific transcription clusters. We provide transcription factor-mediated functional building blocks for cell identity that serve as a global signature for cell types.

Rearrangement of T Cell genome architecture regulates GVHD.

WAPL, cohesin's DNA release factor, regulates three-dimensional (3D) chromatin architecture. The 3D chromatin structure and its relevance to mature T cell functions is not well understood. We show that in vivo lymphopenic expansion, and alloantigen-driven proliferation, alters the 3D structure and function of the genome in mature T cells. Conditional deletion of WAPL, cohesin's DNA release factor, in T cells reduced long-range genomic interactions and altered chromatin A/B compartments and interactions within topologically associating domains (TADs) of the chromatin in T cells at baseline. WAPL deficiency in T cells reduced loop extensions, changed expression of cell cycling genes and reduced proliferation following in vitro and in vivo stimulation, and reduced severity of graft-versus-host disease (GVHD) following experimental allogeneic hematopoietic stem cell transplantation. These data collectively characterize 3D genomic architecture of T cells in vivo and demonstrate biological and clinical implications for its disruption by cohesin release factor WAPL.

TCF7L2 silencing results in altered gene expression patterns accompanied by local genomic reorganization.

Canonical Wnt signaling is crucial for intestinal homeostasis as TCF4, the major Wnt signaling effector in the intestines, is required for stem cell maintenance. The capability of TCF4 to maintain the stem cell phenotype is contingent upon ß-catenin, a potent transcriptional activator, which interacts with histone acetyltransferases and chromatin remodeling complexes. We used RNAi to explore the influence of TCF4 on chromatin structure (Hi-C) and gene expression (RNA sequencing) across a 72-hour time series in colon cancer. We found that TCF4 reduction results in a disproportionate up-regulation of gene expression, including a powerful induction of SOX2. Integration of RNA sequencing and Hi-C data revealed a TAD boundary loss, which occurred concomitantly with the over-expression of a cluster of CEACAM genes on chromosome 19. We identified EMT and E2F as the 2 most deregulated pathways upon TCF4 depletion and LUM, TMPO, and AURKA as highly influential genes in these networks using measures of centrality. Results from gene expression, chromatin structure, and centrality analyses were integrated to generate a list of candidate transcription factors crucial for colon cancer cell homeostasis. The top ranked factor was c-JUN, an oncoprotein known to interact with TCF4 and ß-catenin, confirming the usefulness of this approach.

Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum.

We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our Open Educational Resources on GitHub.

Functional organization of the maternal and paternal human 4D Nucleome.

Every human somatic cell inherits a maternal and a paternal genome, which work together to give rise to cellular phenotypes. However, the allele-specific relationship between gene expression and genome structure through the cell cycle is largely unknown. By integrating haplotype-resolved genome-wide chromosome conformation capture, mature and nascent mRNA, and protein binding data from a B lymphoblastoid cell line, we investigate this relationship both globally and locally. We introduce the maternal and paternal 4D Nucleome, enabling detailed analysis of the mechanisms and dynamics of genome structure and gene function for diploid organisms. Our analyses find significant coordination between allelic expression biases and local genome conformation, and notably absent expression bias in universally essential cell cycle and glycolysis genes. We propose a model in which coordinated biallelic expression reflects prioritized preservation of essential gene sets.

Cellular reprogramming: Mathematics meets medicine.

Generating needed cell types using cellular reprogramming is a promising strategy for restoring tissue function in injury or disease. A common method for reprogramming is addition of one or more transcription factors that confer a new function or identity. Advancements in transcription factor selection and delivery have culminated in successful grafting of autologous reprogrammed cells, an early demonstration of their clinical utility. Though cellular reprogramming has been successful in a number of settings, identification of appropriate transcription factors for a particular transformation has been challenging. Computational methods enable more sophisticated prediction of relevant transcription factors for reprogramming by leveraging gene expression data of initial and target cell types, and are built on mathematical frameworks ranging from information theory to control theory. This review highlights the utility and impact of these mathematical frameworks in the field of cellular reprogramming. This article is categorized under: Reproductive System Diseases > Reproductive System Diseases>Genetics/Genomics/Epigenetics Reproductive System Diseases > Reproductive System Diseases>Stem Cells and Development Reproductive System Diseases > Reproductive System Diseases>Computational Models.

Draft Genome Sequence of a Klebsiella pneumoniae Carbapenemase-Positive Sequence Type 111 Pseudomonas aeruginosa Strain.

Here, we report the draft genome sequence of a sequence type 111 Pseudomonas aeruginosa strain isolated in 2014 from a patient at the NIH Clinical Center. This P. aeruginosa strain exhibits pan-drug resistance and harbors the blaKPC-2 gene, encoding the Klebsiella pneumoniae carbapenemase enzyme, on a plasmid.