Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes.

A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that simulates the computation process of two functions: a feature function defined by a two-layered model on chemical graphs and a prediction function constructed by a machine learning method. To improve the learning performance of prediction functions in the framework, we design a method that splits a given data set C into two subsets C(i),i=1,2 by a hyperplane in a chemical space so that most compounds in the first (resp., second) subset have observed values lower (resp., higher) than a threshold θ. We construct a prediction function ψ to the data set C by combining prediction functions ψi,i=1,2 each of which is constructed on C(i) independently. The results of our computational experiments suggest that the proposed method improved the learning performance for several chemical properties to which a good prediction function has been difficult to construct.

De novo Phased Genome Assembly, Annotation and Population Genotyping of Alectoris Chukar.

The Alectoris Chukar (chukar) is the most geographically widespread partridge species in the world, demonstrating exceptional adaptability to diverse ecological environments. However, the scarcity of genetic resources for chukar has hindered research into its adaptive evolution and molecular breeding. In this study, we have sequenced and assembled a high-quality, phased chukar genome that consists of 31 pairs of relatively complete diploid chromosomes. Our BUSCO analysis reported a high completeness score of 96.8% and 96.5%, with respect to universal single-copy orthologs and a low duplication rate (0.3% and 0.5%) for two assemblies. Through resequencing and population genomic analyses of six subspecies, we have curated invaluable genotype data that underscores the adaptive evolution of chukar in response to both arid and high-altitude environments. These data will significantly contribute to research on how chukars adaptively evolve to cope with desertification and alpine climates.

Dynamic distribution of gut microbiota during Alzheimer's disease progression in a mice model.

Alzheimer's disease (AD) is an irreversible neurodegenerative disease that affects more than 44 million people worldwide. The pathogenic mechanisms of AD still remain unclear. Currently, there are numerous studies investigating the microbiota-gut-brain axis in humans and rodents indicated that gut microbiota played a role in neurodegenerative diseases, including AD. However, the underlying relationship between the progress of AD disease and the dynamic distribution of gut microbiota is not well understood. In the present study, APPswe /PS1ΔE9 transgenic mice of different ages and sex were employed. After the evaluation of the AD mice model, gut metagenomic sequencing was conducted to reveal gut microbiota, moreover, probiotics intervention was treated in the AD mice. The results showed that (1) AD mice had reduced microbiota richness and a changed gut microbiota composition, and AD mice gut microbiota richness was correlated with cognitive performance. We have also found some potential AD-related microbes, for example, in AD-prone mice, the genus Mucispirillum was strongly associated with immune inflammation. (2) Probiotics intervention improved cognitive performance and changed gut microbiota richness and composition of AD mice. We revealed the dynamics distribution of gut microbiota and the effect of probiotics on AD in a mice model, which provides an important reference for the pathogenesis of AD, intestinal microbial markers associated with AD, and AD probiotic intervention.

One-Pot Visual Detection of African Swine Fever Virus Using CRISPR-Cas12a.

African swine fever virus (ASFV) is a leading cause of worldwide agricultural loss. ASFV is a highly contagious and lethal disease for both domestic and wild pigs, which has brought enormous economic losses to a number of countries. Conventional methods, such as general polymerase chain reaction and isothermal amplification, are time-consuming, instrument-dependent, and unsatisfactorily accurate. Therefore, rapid, sensitive, and field-deployable detection of ASFV is important for disease surveillance and control. Herein, we created a one-pot visual detection system for ASFV with CRISPR/Cas12a technology combined with LAMP or RPA. A mineral oil sealing strategy was adopted to mitigate sample cross-contamination between parallel vials during high-throughput testing. Furthermore, the blue fluorescence signal produced by ssDNA reporter could be observed by the naked eye without any dedicated instrument. For CRISPR-RPA system, detection could be completed within 40 min with advantageous sensitivity. While CRISPR-LAMP system could complete it within 60 min with a high sensitivity of 5.8 × 102 copies/µl. Furthermore, we verified such detection platforms display no cross-reactivity with other porcine DNA or RNA viruses. Both CRISPR-RPA and CRISPR-LAMP systems permit highly rapid, sensitive, specific, and low-cost Cas12a-mediated visual diagnostic of ASFV for point-of-care testing (POCT) applications.

Dynamics of Small Non-coding RNA Profiles and the Intestinal Microbiome of High and Low Weight Chickens.

The host and its symbiotic bacteria form a biological entity, holobiont, in which they share a dynamic connection characterized by symbiosis, co-metabolism, and coevolution. However, how these collaborative relationships were maintained over evolutionary time remains unclear. In this research, the small non-coding RNA (sncRNA) profiles of cecum and their bacteria contents were measured from lines of chickens that have undergone long-term selection for high (HWS) or low (LWS) 56-day body weight. The results from these lines that originated from a common founder population and maintained under the same husbandry showed an association between host intestinal sncRNA expression profile (miRNA, lncRNA fragment, mRNA fragment, snoRNA, and snRNA) and intestinal microbiota. Correlation analyses suggested that some central miRNAs and mRNA fragments had interactions with the abundance of intestinal microbial species and microbiota functions. miR-6622-3p, a significantly differentially expressed (DE) miRNA was correlated with a body weight gain related bacterium, Alistipes putredinis. Our results showed that host sncRNAs may be mediators of interaction between the host and its intestinal microbiome. This provides additional clue for holobiont concepts.

An Inverse QSAR Method Based on Linear Regression and Integer Programming.

BACKGROUND: Drug design is one of the important applications of biological science. Extensive studies have been done on computer-aided drug design based on inverse quantitative structure activity relationship (inverse QSAR), which is to infer chemical compounds from given chemical activities and constraints. However, exact or optimal solutions are not guaranteed in most of the existing methods. METHOD: Recently a novel framework based on artificial neural networks (ANNs) and mixed integer linear programming (MILP) has been proposed for designing chemical structures. This framework consists of two phases: an ANN is used to construct a prediction function, and then an MILP formulated on the trained ANN and a graph search algorithm are used to infer desired chemical structures. In this paper, we use linear regression instead of ANNs to construct a prediction function. For this, we derive a novel MILP formulation that simulates the computation process of a prediction function by linear regression. RESULTS: For the first phase, we performed computational experiments using 18 chemical properties, and the proposed method achieved good prediction accuracy for a relatively large number of properties, in comparison with ANNs in our previous work. For the second phase, we performed computational experiments on five chemical properties, and the method could infer chemical structures with around up to 50 non-hydrogen atoms. CONCLUSIONS: Combination of linear regression and integer programming is a potentially useful approach to computational molecular design.

The functional role of fecal microbiota transplantation on Salmonella Enteritidis infection in chicks.

The intestinal microbiota plays important roles in animal health and growth. We investigated the efficacy and mechanisms of fecal microbiota transplantation (FMT) from adult SPF chickens against Salmonella Enteritidis (SE) infection in chicks. We transplanted 160 recipient SPF chicks (1-day-old) that were randomly divided into four groups, Ca (challenge), Cb (non-challenge), Fa (FMT and challenge) and Fb (FMT without challenge). The experiment lasted 40 days. We found that FMT reduced mortality as well as liver inflammatory lesions, promoted weight gain, improved immunity, ameliorated the digestion and absorption ability and inhibited SE colonization in the liver of challenged chicks. 16S rRNA gene high-throughput sequencing indicated that SE challenge caused a significant increase in the relative abundance of Parasutterella in the cecal microbiota of the recipient chicks (P < 0.05). FMT led to the maturation of the intestinal flora of recipients and the relative abundance of the Bacteroides, Rikenellaceae_ RC9_ gut_ group, Prevotellaceae_ UCG_ 001, Prevotellaceae_ Ga6A1_ group and Parabacteroides was significantly increased (P < 0.05). FMT from adult SPF chickens regulated the intestinal microbiota of chicks and increased resistance to SE infection.

A Novel Method for Inferring Chemical Compounds With Prescribed Topological Substructures Based on Integer Programming.

Drug discovery is one of the major goals of computational biology and bioinformatics. A novel framework has recently been proposed for the design of chemical graphs using both artificial neural networks (ANNs) and mixed integer linear programming (MILP). This method consists of a prediction phase and an inverse prediction phase. In the first phase, an ANN is trained using data on existing chemical compounds. In the second phase, given a target chemical property, a feature vector is inferred by solving an MILP formulated from the trained ANN and then a set of chemical structures is enumerated by a graph enumeration algorithm. Although exact solutions are guaranteed by this framework, the types of chemical graphs have been restricted to such classes as trees, monocyclic graphs, and graphs with a specified polymer topology with cycle index up to 2. To overcome the limitation on the topological structure, we propose a new flexible modeling method to the framework so that we can specify a topological substructure of graphs and a partial assignment of chemical elements and bond-multiplicity to a target graph. The results of computational experiments suggest that the proposed system can infer chemical graphs with around up to 50 non-hydrogen atoms.

A novel method for inference of acyclic chemical compounds with bounded branch-height based on artificial neural networks and integer programming.

Analysis of chemical graphs is becoming a major research topic in computational molecular biology due to its potential applications to drug design. One of the major approaches in such a study is inverse quantitative structure activity/property relationship (inverse QSAR/QSPR) analysis, which is to infer chemical structures from given chemical activities/properties. Recently, a novel two-phase framework has been proposed for inverse QSAR/QSPR, where in the first phase an artificial neural network (ANN) is used to construct a prediction function. In the second phase, a mixed integer linear program (MILP) formulated on the trained ANN and a graph search algorithm are used to infer desired chemical structures. The framework has been applied to the case of chemical compounds with cycle index up to 2 so far. The computational results conducted on instances with n non-hydrogen atoms show that a feature vector can be inferred by solving an MILP for up to [Formula: see text], whereas graphs can be enumerated for up to [Formula: see text]. When applied to the case of chemical acyclic graphs, the maximum computable diameter of a chemical structure was up to 8. In this paper, we introduce a new characterization of graph structure, called "branch-height" based on which a new MILP formulation and a new graph search algorithm are designed for chemical acyclic graphs. The results of computational experiments using such chemical properties as octanol/water partition coefficient, boiling point and heat of combustion suggest that the proposed method can infer chemical acyclic graphs with around [Formula: see text] and diameter 30.

An Inverse QSAR Method Based on a Two-Layered Model and Integer Programming.

A novel framework for inverse quantitative structure-activity relationships (inverse QSAR) has recently been proposed and developed using both artificial neural networks and mixed integer linear programming. However, classes of chemical graphs treated by the framework are limited. In order to deal with an arbitrary graph in the framework, we introduce a new model, called a two-layered model, and develop a corresponding method. In this model, each chemical graph is regarded as two parts: the exterior and the interior. The exterior consists of maximal acyclic induced subgraphs with bounded height, the interior is the connected subgraph obtained by ignoring the exterior, and the feature vector consists of the frequency of adjacent atom pairs in the interior and the frequency of chemical acyclic graphs in the exterior. Our method is more flexible than the existing method in the sense that any type of graphs can be inferred. We compared the proposed method with an existing method using several data sets obtained from PubChem database. The new method could infer more general chemical graphs with up to 50 non-hydrogen atoms. The proposed inverse QSAR method can be applied to the inference of more general chemical graphs than before.

Transcriptomic Analysis of MSTN Knockout in the Early Differentiation of Chicken Fetal Myoblasts.

In mammals, Myostatin (MSTN) is a known negative regulator of muscle growth and development, but its role in birds is poorly understood. To investigate the molecular mechanism of MSTN on muscle growth and development in chickens, we knocked out MSTN in chicken fetal myoblasts (CFMs) and sequenced the mRNA transcriptomes. The amplicon sequencing results show that the editing efficiency of the cells was 76%. The transcriptomic results showed that 296 differentially expressed genes were generated after down-regulation of MSTN, including angiotensin I converting enzyme (ACE), extracellular fatty acid-binding protein (EXFABP) and troponin T1, slow skeletal type (TNNT1). These genes are closely associated with myoblast differentiation, muscle growth and energy metabolism. Subsequent enrichment analysis showed that DEGs of CFMs were related to MAPK, Pl3K/Akt, and STAT3 signaling pathways. The MAPK and Pl3K/Akt signaling pathways are two of the three known signaling pathways involved in the biological effects of MSTN in mammals, and the STAT3 pathway is also significantly enriched in MSTN knock out chicken leg muscles. The results of this study will help to understand the possible molecular mechanism of MSTN regulating the early differentiation of CFMs and lay a foundation for further research on the molecular mechanism of MSTN involvement in muscle growth and development.

Emerging role of long non-coding RNA MALAT1 in predicting clinical outcomes of patients with digestive system malignancies: A meta-analysis.

Digestive system malignancies are the most common cancer types worldwide and exhibit an extremely low overall 5-year survival rate. Therefore, clinically applicable biomarkers for predicting clinical outcome are urgently required. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is abnormally expressed in several cancer types. However, to the best of our knowledge, the association between MALAT1 expression and the prognosis of digestive system malignancies remains unknown. Therefore, the current study performed a meta-analysis to comprehensively summarize the association between MALAT1 expression and digestive system malignancies. A total of 1,157 Asian patients from 12 eligible studies [eight studies that investigated overall survival (OS), two studies that investigated disease-free survival and two studies that investigated both indicators] were analyzed. The present results identified a significant association between MALAT1 abundance and poor OS in patients with digestive system malignancies, with a pooled hazard ratio (HR) of 1.62 [95% confidence interval (CI), 1.35-1.88; P<0.001]. The tumor type, region, sample size and analysis type did not alter the predictive value of MALAT1 as an independent factor for survival. Furthermore, MALAT1 overexpression was an unfavorable prognostic factor for the overall survival of patients with esophageal carcinoma, pancreatic cancer, hepatocellular carcinoma and gastric cancer, with HRs of 1.89 (95% CI, 1.29-2.49), 1.76 (95% CI, 0.89-2.63), 1.46 (95% CI, 0.76-2.17) and 1.41 (95% CI, 1.04-1.78), respectively. In particular, increased MALAT1 expression levels were significantly associated with decreased OS in patients with colorectal cancer (HR, 3.04; 95% CI, 1.77-4.31). In conclusion, lncRNA MALAT1 may be a potential prognostic factor for digestive system malignancies in Asian populations.