Successful omental flap coverage repair of a rectovaginal fistula after low anterior resection: a case report.

BACKGROUND: Rectovaginal fistula (RVF) is a troublesome and refractory complication after low anterior resection (LAR) for rectal cancer. An omental flap repair was performed for the RVF caused due to Crohn's disease and childbirth trauma. However, there are few cases of an omental flap repair for RVF after LAR. Herein, we present a successfully repaired case of RVF by omental flap coverage after LAR for rectal cancer. CASE PRESENTATION: A 50-year-old female patient with advanced rectal cancer underwent laparoscopic LAR with double-stapling technique anastomosis and achieved curative resection. She complained of a stool from the vagina and was diagnosed with RVF on the postoperative day (POD) 18. Conservative therapy was ineffective. We performed laparoscopic fistula resection and direct closure of the vagina and rectum, designed the omentum that could reach the pelvis, repaired RVF by omental flap coverage, and performed transverse colostomy on POD 25. She was discharged on initial POD 48. Seven months after the initial operation, colostomy closure was administered. There was no recurrence of RVF found 1 year after the initial operation. CONCLUSIONS: The patient achieved an omental flap coverage for RVF. We successfully performed the omental flap coverage repair in patients with RVF after the leakage of LAR. An omental flap may become an alternative treatment for muscle flap or an effective treatment for RVF.

[Hemorrhagic Colon Cancer with Left Atrial Thrombus Formation after Anticoagulant Therapy Discontinuation-A Case Report].

The patient was a 72-year-old female. She had been taking rivaroxaban for chronic atrial fibrillation; however, she stopped taking it due to anemia and was hospitalized urgently. A contrast-enhanced computed tomography(CT)scan showed a 30 mm mass in the ascending colon, and a colonoscopy revealed ascending colon cancer(cT3, cN0, cM0, cStage Ⅱa). The tumor was hemorrhagic and was thought to have caused the anemia. On day 6 of hospitalization, another contrast- enhanced CT scan showed a poorly contrast-enhanced area in the left atrium, and transesophageal echocardiography revealed 2 left atrial thrombi(27 mm and 17 mm). Since early induction of anticoagulation therapy was considered, an emergency open right colectomy was performed to remove the cause of the bleeding. Intravenous heparin therapy was started the day after surgery and was switched to oral apixaban therapy on the fourth postoperative day. The postoperative course was good, and she was discharged home on the 17th postoperative day. This patient had conflicting clinical problems simultaneously; however, immediate decision-making and initiation of treatment were effective.

Inferring Multidimensional Rates of Aging from Cross-Sectional Data.

Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained to be order-isomorphic, the model family is identifiable solely from cross-sectional data provided the distribution of time-independent variation is known. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors.

DNase-capture reveals differential transcription factor binding modalities.

We describe DNase-capture, an assay that increases the analytical resolution of DNase-seq by focusing its sequencing phase on selected genomic regions. We introduce a new method to compensate for capture bias called BaseNormal that allows for accurate recovery of transcription factor protection profiles from DNase-capture data. We show that these normalized data allow for nuanced detection of transcription factor binding heterogeneity with as few as dozens of sites.

A synergistic DNA logic predicts genome-wide chromatin accessibility.

Enhancers and promoters commonly occur in accessible chromatin characterized by depleted nucleosome contact; however, it is unclear how chromatin accessibility is governed. We show that log-additive cis-acting DNA sequence features can predict chromatin accessibility at high spatial resolution. We develop a new type of high-dimensional machine learning model, the Synergistic Chromatin Model (SCM), which when trained with DNase-seq data for a cell type is capable of predicting expected read counts of genome-wide chromatin accessibility at every base from DNA sequence alone, with the highest accuracy at hypersensitive sites shared across cell types. We confirm that a SCM accurately predicts chromatin accessibility for thousands of synthetic DNA sequences using a novel CRISPR-based method of highly efficient site-specific DNA library integration. SCMs are directly interpretable and reveal that a logic based on local, nonspecific synergistic effects, largely among pioneer TFs, is sufficient to predict a large fraction of cellular chromatin accessibility in a wide variety of cell types.

Cas9 Functionally Opens Chromatin.

Using a nuclease-dead Cas9 mutant, we show that Cas9 reproducibly induces chromatin accessibility at previously inaccessible genomic loci. Cas9 chromatin opening is sufficient to enable adjacent binding and transcriptional activation by the settler transcription factor retinoic acid receptor at previously unbound motifs. Thus, we demonstrate a new use for Cas9 in increasing surrounding chromatin accessibility to alter local transcription factor binding.

GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding.

MOTIVATION: The majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies. RESULTS: We present GERV (generative evaluation of regulatory variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor's canonical motif and associated co-factor motifs. We show that GERV outperforms existing methods in predicting single-nucleotide polymorphisms associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked single-nucleotide polymorphisms and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis. AVAILABILITY AND IMPLEMENTATION: The implementation of GERV and related data are available at http://gerv.csail.mit.edu/.

Cloning-free CRISPR.

We present self-cloning CRISPR/Cas9 (scCRISPR), a technology that allows for CRISPR/Cas9-mediated genomic mutation and site-specific knockin transgene creation within several hours by circumventing the need to clone a site-specific single-guide RNA (sgRNA) or knockin homology construct for each target locus. We introduce a self-cleaving palindromic sgRNA plasmid and a short double-stranded DNA sequence encoding the desired locus-specific sgRNA into target cells, allowing them to produce a locus-specific sgRNA plasmid through homologous recombination. scCRISPR enables efficient generation of gene knockouts (∼88% mutation rate) at approximately one-sixth the cost of plasmid-based sgRNA construction with only 2 hr of preparation for each targeted site. Additionally, we demonstrate efficient site-specific knockin of GFP transgenes without any plasmid cloning or genome-integrated selection cassette in mouse and human embryonic stem cells (2%-4% knockin rate) through PCR-based addition of short homology arms. scCRISPR substantially lowers the bar on mouse and human transgenesis.

Universal count correction for high-throughput sequencing.

We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called FIXSEQ. We demonstrate that FIXSEQ substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives.

Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape.

We describe protein interaction quantitation (PIQ), a computational method for modeling the magnitude and shape of genome-wide DNase I hypersensitivity profiles to identify transcription factor (TF) binding sites. Through the use of machine-learning techniques, PIQ identified binding sites for >700 TFs from one DNase I hypersensitivity analysis followed by sequencing (DNase-seq) experiment with accuracy comparable to that of chromatin immunoprecipitation followed by sequencing (ChIP-seq). We applied PIQ to analyze DNase-seq data from mouse embryonic stem cells differentiating into prepancreatic and intestinal endoderm. We identified 120 and experimentally validated eight 'pioneer' TF families that dynamically open chromatin. Four pioneer TF families only opened chromatin in one direction from their motifs. Furthermore, we identified 'settler' TFs whose genomic binding is principally governed by proximity to open chromatin. Our results support a model of hierarchical TF binding in which directional and nondirectional pioneer activity shapes the chromatin landscape for population by settler TFs.

Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates.

Countless studies monitor the growth rate of microbial populations as a measure of fitness. However, an enormous gap separates growth-rate differences measurable in the laboratory from those that natural selection can distinguish efficiently. Taking advantage of the recent discovery that transcript and protein levels in budding yeast closely track growth rate, we explore the possibility that growth rate can be more sensitively inferred by monitoring the proteomic response to growth, rather than growth itself. We find a set of proteins whose levels, in aggregate, enable prediction of growth rate to a higher precision than direct measurements. However, we find little overlap between these proteins and those that closely track growth rate in other studies. These results suggest that, in yeast, the pathways that set the pace of cell division can differ depending on the growth-altering stimulus. Still, with proper validation, protein measurements can provide high-precision growth estimates that allow extension of phenotypic growth-based assays closer to the limits of evolutionary selection.

Lineage-based identification of cellular states and expression programs.

We present a method, LineageProgram, that uses the developmental lineage relationship of observed gene expression measurements to improve the learning of developmentally relevant cellular states and expression programs. We find that incorporating lineage information allows us to significantly improve both the predictive power and interpretability of expression programs that are derived from expression measurements from in vitro differentiation experiments. The lineage tree of a differentiation experiment is a tree graph whose nodes describe all of the unique expression states in the input expression measurements, and edges describe the experimental perturbations applied to cells. Our method, LineageProgram, is based on a log-linear model with parameters that reflect changes along the lineage tree. Regularization with L(1) that based methods controls the parameters in three distinct ways: the number of genes change between two cellular states, the number of unique cellular states, and the number of underlying factors responsible for changes in cell state. The model is estimated with proximal operators to quickly discover a small number of key cell states and gene sets. Comparisons with existing factorization, techniques, such as singular value decomposition and non-negative matrix factorization show that our method provides higher predictive power in held, out tests while inducing sparse and biologically relevant gene sets.

Tree preserving embedding.

The goal of dimensionality reduction is to embed high-dimensional data in a low-dimensional space while preserving structure in the data relevant to exploratory data analysis such as clusters. However, existing dimensionality reduction methods often either fail to separate clusters due to the crowding problem or can only separate clusters at a single resolution. We develop a new approach to dimensionality reduction: tree preserving embedding. Our approach uses the topological notion of connectedness to separate clusters at all resolutions. We provide a formal guarantee of cluster separation for our approach that holds for finite samples. Our approach requires no parameters and can handle general types of data, making it easy to use in practice and suggesting new strategies for robust data visualization.

BFL: a node and edge betweenness based fast layout algorithm for large scale networks.

BACKGROUND: Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks, e.g. more than 10000 elements. RESULTS: To overcome these problems, we propose the use of a biologically important graph metric, betweenness, a measure of network flow. This metric is highly correlated with many biological phenomena such as lethality and clusters. We devise a new fast parallel algorithm calculating betweenness to minimize the preprocessing cost. Using this metric, we also invent a node and edge betweenness based fast layout algorithm (BFL). BFL places the high-betweenness nodes to optimal positions and allows the low-betweenness nodes to reach suboptimal positions. Furthermore, BFL reduces the runtime by combining a sequential insertion algorim with betweenness. For a graph with n nodes, this approach reduces the expected runtime of the algorithm to O(n2) when considering edge crossings, and to O(n log n) when considering only density and edge lengths. CONCLUSION: Our BFL algorithm is compared against fast graph layout algorithms and approaches requiring intensive optimizations. For gene networks, we show that our algorithm is faster than all layout algorithms tested while providing readability on par with intensive optimization algorithms. We achieve a 1.4 second runtime for a graph with 4000 nodes and 12000 edges on a standard desktop computer.