Replication timing networks reveal a link between transcription regulatory circuits and replication timing control.

DNA replication occurs in a defined temporal order known as the replication timing (RT) program and is regulated during development, coordinated with 3D genome organization and transcriptional activity. However, transcription and RT are not sufficiently coordinated to predict each other, suggesting an indirect relationship. Here, we exploit genome-wide RT profiles from 15 human cell types and intermediate differentiation stages derived from human embryonic stem cells to construct different types of RT regulatory networks. First, we constructed networks based on the coordinated RT changes during cell fate commitment to create highly complex RT networks composed of thousands of interactions that form specific functional subnetwork communities. We also constructed directional regulatory networks based on the order of RT changes within cell lineages, and identified master regulators of differentiation pathways. Finally, we explored relationships between RT networks and transcriptional regulatory networks (TRNs) by combining them into more complex circuitries of composite and bipartite networks. Results identified novel trans interactions linking transcription factors that are core to the regulatory circuitry of each cell type to RT changes occurring in those cell types. These core transcription factors were found to bind cooperatively to sites in the affected replication domains, providing provocative evidence that they constitute biologically significant directional interactions. Our findings suggest a regulatory link between the establishment of cell-type-specific TRNs and RT control during lineage specification.

Outcome and quality of life in obese patients underwent laparoscopic vs. open appendectomy.

BACKGROUND: Although obesity is a popular reason for choosing laparoscopic appendectomy (LA) versus open appendectomy (OA), however, the question of whether there is a difference remains. Our goal is to investigate if there is a difference between OA and LA in obese patients. METHODS: Fifty-eight obese patients diagnosed with acute appendicitis according to ALVARDO score at department of surgery at Suez Canal university hospitals from March 2020 till August 2021 were included. The study participants were assigned in two groups LA and OA. This study aimed to comparing between LA and OA regarding intraoperative complications, length of hospital stays, post -operative pain, and rate of post-operative complications. Meanwhile, using SF-36 scoring questionnaire, the quality of life was compared between both groups. RESULTS: A total of 58 patients were included in the present study (LG = 29 patients and OG = 29 patients). The early post-operative complications (within 30 days after surgery) were significantly lower in the LA group (5 patients out of 29) than the OA (11 patients out of 29). Additionally, lower incidence of complications was noticed in the LA group (2 out of 29 patients) compared to OA (6 patients out of 29) beyond 30 days after operation. Patients with laparoscopic surgery had statistically significant higher overall quality of life scores (SF-36) (72 ± 32) compared to open surgery patients (66 ± 35) 2 weeks after operation. CONCLUSION: The laparoscopic procedure was associated with lower incidence of post operative complications. However, open appendectomy was superior for a shorter operative time. Laparoscopic approach is not only used for therapeutic purposes, but also it has a diagnostic role.

Dynamic changes in replication timing and gene expression during lineage specification of human pluripotent stem cells.

Duplication of the genome in mammalian cells occurs in a defined temporal order referred to as its replication-timing (RT) program. RT changes dynamically during development, regulated in units of 400-800 kb referred to as replication domains (RDs). Changes in RT are generally coordinated with transcriptional competence and changes in subnuclear position. We generated genome-wide RT profiles for 26 distinct human cell types, including embryonic stem cell (hESC)-derived, primary cells and established cell lines representing intermediate stages of endoderm, mesoderm, ectoderm, and neural crest (NC) development. We identified clusters of RDs that replicate at unique times in each stage (RT signatures) and confirmed global consolidation of the genome into larger synchronously replicating segments during differentiation. Surprisingly, transcriptome data revealed that the well-accepted correlation between early replication and transcriptional activity was restricted to RT-constitutive genes, whereas two-thirds of the genes that switched RT during differentiation were strongly expressed when late replicating in one or more cell types. Closer inspection revealed that transcription of this class of genes was frequently restricted to the lineage in which the RT switch occurred, but was induced prior to a late-to-early RT switch and/or down-regulated after an early-to-late RT switch. Analysis of transcriptional regulatory networks showed that this class of genes contains strong regulators of genes that were only expressed when early replicating. These results provide intriguing new insight into the complex relationship between transcription and RT regulation during human development.

Signal reachability facilitates characterization of probabilistic signaling networks.

BACKGROUND: Studying biological networks is of extreme importance in understanding cellular functions. These networks model interactions between molecules in each cell. A large volume of research has been done to uncover different characteristics of biological networks, such as large-scale organization, node centrality and network robustness. Nevertheless, the vast majority of research done in this area assume that biological networks have deterministic topologies. Biological interactions are however probabilistic events that may or may not appear at different cells or even in the same cell at different times. RESULTS: In this paper, we present novel methods for characterizing probabilistic signaling networks. Our methods do this by computing the probability that a signal propagates successfully from receptor to reporter genes through interactions in the network. We characterize such networks with respect to (i) centrality of individual nodes, (ii) stability of the entire network, and (iii) important functions served by the network. We use these methods to characterize major H. sapiens signaling networks including Wnt, ErbB and MAPK.

Large scale analysis of signal reachability.

MOTIVATION: Major disorders, such as leukemia, have been shown to alter the transcription of genes. Understanding how gene regulation is affected by such aberrations is of utmost importance. One promising strategy toward this objective is to compute whether signals can reach to the transcription factors through the transcription regulatory network (TRN). Due to the uncertainty of the regulatory interactions, this is a #P-complete problem and thus solving it for very large TRNs remains to be a challenge. RESULTS: We develop a novel and scalable method to compute the probability that a signal originating at any given set of source genes can arrive at any given set of target genes (i.e., transcription factors) when the topology of the underlying signaling network is uncertain. Our method tackles this problem for large networks while providing a provably accurate result. Our method follows a divide-and-conquer strategy. We break down the given network into a sequence of non-overlapping subnetworks such that reachability can be computed autonomously and sequentially on each subnetwork. We represent each interaction using a small polynomial. The product of these polynomials express different scenarios when a signal can or cannot reach to target genes from the source genes. We introduce polynomial collapsing operators for each subnetwork. These operators reduce the size of the resulting polynomial and thus the computational complexity dramatically. We show that our method scales to entire human regulatory networks in only seconds, while the existing methods fail beyond a few tens of genes and interactions. We demonstrate that our method can successfully characterize key reachability characteristics of the entire transcriptions regulatory networks of patients affected by eight different subtypes of leukemia, as well as those from healthy control samples. AVAILABILITY: All the datasets and code used in this article are available at bioinformatics.cise.ufl.edu/PReach/scalable.htm.

Multicenter validation of an artificial intelligence (AI)-based platform for the diagnosis of acute appendicitis.

BACKGROUND: The current scores used to help diagnose acute appendicitis have a "gray" zone in which the diagnosis is usually inconclusive. Furthermore, the universal use of CT scanning is limited because of the radiation hazards and/or limited resources. Hence, it is imperative to have an accurate diagnostic tool to avoid unnecessary, negative appendectomies. METHODS: This was an international, multicenter, retrospective cohort study. The diagnostic accuracy of the artificial intelligence platform was assessed by sensitivity, specificity, negative predictive value, the area under the receiver curve, precision curve, F1 score, and Matthews correlation coefficient. Moreover, calibration curve, decision curve analysis, and clinical impact curve analysis were used to assess the clinical utility of the artificial intelligence platform. The accuracy of the artificial intelligence platform was also compared to that of CT scanning. RESULTS: Two data sets were used to assess the artificial intelligence platform: a multicenter real data set (n = 2,579) and a well-qualified synthetic data set (n = 9736). The platform showed a sensitivity of 92.2%, specificity of 97.2%, and negative predictive value of 98.7%. The artificial intelligence had good area under the receiver curve, precision, F1 score, and Matthews correlation coefficient (0.97, 86.7, 0.89, 0.88, respectively). Compared to CT scanning, the artificial intelligence platform had a better area under the receiver curve (0.92 vs 0.76), specificity (90.9 vs 53.3), precision (99.8 vs 98.9), and Matthews correlation coefficient (0.77 vs 0.72), comparable sensitivity (99.2 vs 100), and lower negative predictive value (67.6 vs 99.5). Decision curve analysis and clinical impact curve analysis intuitively revealed that the platform had a substantial net benefit within a realistic probability range from 6% to 96%. CONCLUSION: The current artificial intelligence platform had excellent sensitivity, specificity, and accuracy exceeding 90% and may help clinicians in decision making on patients with suspected acute appendicitis, particularly when access to CT scanning is limited.

Efficacy and Safety of Microwave Ablation (MWA) for Hepatocellular Carcinoma (HCC) in Difficult Anatomical Sites in Egyptian Patients with Liver Cirrhosis

Background and aim: Imaging guided microwave ablation (MWA) for hepatocellular carcinoma (HCC) has become a widely used method over recent years. Tumors close to the diaphragm, gastrointestinal tract, gallbladder, pancreas, hepatic hilum and major bile duct or vessels are generally considered relative contraindications for microwave ablation. This study was conducted to assess the effectiveness and safety of ultrasonography-guided MWA in treating patients with HCC in difficult anatomical sites in comparison to those in conventional sites. Patients and methods: Eighty-eight patients were included and divided into two groups: the study group of 44 with 46 lesions lying <5mm from the diaphragm, hepatic capsule, gall bladder (GB) or large vessel; and the control group of 44 patients with 50 lesions in non-risky sites. Each lesion was ablated using an ultrasound guided microwave probe using a detailed protocol. Results: Most of the patients were males, with a mean age of 57.8 years. In the study group, two patients had lesions adjacent to the GB, twelve were perivascular and 32 were subcapsular. The overall successful ablation rates were 84.8% and 92% in the study and control groups, respectively. Within the study group, ablation rates were 100%, 75% and 87.5% for lesions close to the GB, perivascular lesions and subcapsular lesions, respectively. One patient developed a subcutaneous abscess, with good outcome after proper treatment. Fever, pain and asymptomatic pleural effusion were reported after ablation without statistically significant difference between the groups or among subgroups. In conclusion: MWA for HCC in difficult anatomical sites is as effective and safe as for ordinary sites.

Reachability Analysis in Probabilistic Biological Networks.

Extra-cellular molecules trigger a response inside the cell by initiating a signal at special membrane receptors (i.e., sources), which is then transmitted to reporters (i.e., targets) through various chains of interactions among proteins. Understanding whether such a signal can reach from membrane receptors to reporters is essential in studying the cell response to extra-cellular events. This problem is drastically complicated due to the unreliability of the interaction data. In this paper, we develop a novel method, called PReach (Probabilistic Reachability), that precisely computes the probability that a signal can reach from a given collection of receptors to a given collection of reporters when the underlying signaling network is uncertain. This is a very difficult computational problem with no known polynomial-time solution. PReach represents each uncertain interaction as a bi-variate polynomial. It transforms the reachability problem to a polynomial multiplication problem. We introduce novel polynomial collapsing operators that associate polynomial terms with possible paths between sources and targets as well as the cuts that separate sources from targets. These operators significantly shrink the number of polynomial terms and thus the running time. PReach has much better time complexity than the recent solutions for this problem. Our experimental results on real data sets demonstrate that this improvement leads to orders of magnitude of reduction in the running time over the most recent methods. Availability: All the data sets used, the software implemented and the alignments found in this paper are available at http://bioinformatics.cise.ufl.edu/PReach/.

Computing interaction probabilities in signaling networks.

Biological networks inherently have uncertain topologies. This arises from many factors. For instance, interactions between molecules may or may not take place under varying conditions. Genetic or epigenetic mutations may also alter biological processes like transcription or translation. This uncertainty is often modeled by associating each interaction with a probability value. Studying biological networks under this probabilistic model has already been shown to yield accurate and insightful analysis of interaction data. However, the problem of assigning accurate probability values to interactions remains unresolved. In this paper, we present a novel method for computing interaction probabilities in signaling networks based on transcription levels of genes. The transcription levels define the signal reachability probability between membrane receptors and transcription factors. Our method computes the interaction probabilities that minimize the gap between the observed and the computed signal reachability probabilities. We evaluate our method on four signaling networks from the Kyoto Encyclopedia of Genes and Genomes (KEGG). For each network, we compute its edge probabilities using the gene expression profiles for seven major leukemia subtypes. We use these values to analyze how the stress induced by different leukemia subtypes affects signaling interactions.

Evaluation of fluvastatin in combination with the standard of care therapy (PEG-IFN/Ribavirin) in Egyptian patients with hepatitis C virus.

BACKGROUND/AIMS: Cholesterol biosynthesis suppresses the replication of HCV-1b replicons, thus influencing hepatitis C virus (HCV) natural history. This study aimed at comparing the efficacy and safety of fluvastatin (FLV) as an adjuvant therapy to the standard of care (SOC) therapy, i.e., pegylated interferon (PEG-IFN) and ribavirin, for the treatment of HCV patients. MATERIALS AND METHODS: Sixty HCV patients were enrolled and allocated to either group I, who received the triple therapy (fluvastatin + SOC), or group II, who received SOC; the duration for both treatments was 48 weeks. All patients were subjected to pretreatment liver biopsy and monthly biochemical tests (liver profile, CBC), and quantitative HCV-RNA test was performed at weeks 0, 4, 12, 48, and 72. RESULTS: All virological responses were higher in group I than in group II, with no statistical difference. Group I showed no manifestations of hepatotoxicity. CONCLUSION: Fluvastatin yielded a borderline, significantly higher complete early virological response than SOC; therefore, it is a safe adjuvant to the SOC therapy.

From uncertain protein interaction networks to signaling pathways through intensive color coding.

Discovering signaling pathways in protein interaction networks is a key ingredient in understanding how proteins carry out cellular functions. These interactions however can be uncertain events that may or may not take place depending on many factors including the internal factors, such as the size and abundance of the proteins, or the external factors, such as mutations, disorders and drug intake. In this paper, we consider the problem of finding causal orderings of nodes in such protein interaction networks to discover signaling pathways. We adopt color coding technique to address this problem. Color coding method may fail with some probability. By allowing it to run for sufficient time, however, its confidence in the optimality of the result can converge close to 100%. Our key contribution in this paper is elimination of the key conservative assumptions made by the traditional color coding methods while computing its success probability. We do this by carefully establishing the relationship between node colors, network topology and success probability. As a result our method converges to any confidence value much faster than the traditional methods. Thus, it is scalable to larger protein interaction networks and longer signaling pathways than existing methods. We demonstrate, both theoretically and experimentally that our method outperforms existing methods.