Spatiotemporal Relationship between Ecosystem Service Value and Ecological Risk in Disaster-Prone Mountainous Areas: Taking the Upper Reaches of the Minjiang River as an Example.

Mountainous areas are susceptible to disasters; the frequent occurrence of disasters drives the changes in ecosystem service value (ESV) and also brings certain ecological risk, which further increases the incidence of disasters. However, few scholars have investigated the spatiotemporal correlation between the ESV of disaster-prone mountainous areas and ecological risk index (ERI) with basin as the unit. This paper aims to clarify the spatial relationship between ESV and ERI under the changes of land use. Taking the upper reaches of the Minjiang River as the study area, the authors collected the land use data of 2000-2020, estimated ESV by the value equivalent factor per unit area method, and constructed the ERI. On this basis, the relationship between ESV and ERI was investigated in details. The results show the following: (1) From 2000 to 2020, the total ESV exhibited a fluctuating upward trend. The spatial distribution of ESV was greatly affected by slope and altitude; an important reason for the rising ESV in the study area is the increase of forest area and water area. (2) The upper reaches of the Minjiang River had a generally low ERI and relatively good overall ecoenvironment. After 2010, however, the ecological risk continued to rise. Most of the strongly high risk areas are areas with frequent human activities, such as low-altitude areas and river banks. (3) There is a spatial correlation and coupling between ESV and ERI in the study area; i.e., the strongly high ESV areas generally had a low ecological risk. The correlation intensified with the elapse of time. The changes in the service value of regional ecosystems driven by unreasonable land use will have a great impact on the ecoenvironment. By clarifying the spatiotemporal relationship between ESV and ERI, this research provides theoretical basis and data support to the formulation of ecoenvironmental restoration and protection plans for the upper reaches of the Minjiang River and to the coordinated development between society, economy, and ecoenvironment in the region.

Spatio-temporal evolution and driving factors of carbon storage in the Western Sichuan Plateau.

The carbon sequestration function of the ecosystem is one of the most important functions of ecosystem service, and it of great significance to study the spatio-temporal differentiation of carbon storage for promoting regional sustainable development. Ecosystems on the Western Sichuan Plateau are highly variable, but its spatio-temporal differentiation and driving factors are not yet clear. In this study, on the basis of land use monitoring data, meteorological and demographic data interpreted from Landsat remote sensing image, and through GIS analysis tools, the carbon storage module of InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model was used to estimate carbon storage and geodetector was used to detect the driving factors of carbon storage spatial differentiation. The results show that: (1) The carbon storage increased to 1.2455 × 1010 t from 1.2438 × 1010 t in the past 20 years, the ecosystem developed in a healthy way overall. (2) Carbon storage show High-High and Low-Low aggregation characteristics, but the area decreased by 1481.81 km2 and 311.11 km2 respectively, and the spatial cluster effect gradually weakened. (3) HAI is the leading factor causing the spatio-temporal differentiation of regional carbon storage, followed by temperature and NDVI; the interaction between factors significantly enhances the spatial differentiation of carbon storage, indicating that the change of carbon storage is the result of the joint action of natural and socioeconomic factors. The results of the study provide some theoretical basis for the development of differentiated ecological regulation models and strategies, and help to promote high-quality regional development.

Establishment and resilience of transplanted gut microbiota in aged mice.

The maintenance of healthy and resilient gut microbiota is critical for the life quality and healthspan of the elderly. Fecal microbiota transplantation (FMT) has been increasingly used to restore healthy gut microbiota. We systemically studied the establishment and resilience of transplanted microbiota after autologous versus heterologous FMT in aged recipients. Gut microbiota of aged mice (20 months old) failed to restore their original diversity and composition over 8 weeks via spontaneous recovery after antibiotics treatment; in contrast, FMT using either autologous or heterologous (2 months old from a different vendor) donors facilitated the recovery successfully, established donor-like microbiota states, and affected host gene expression profile. Furthermore, the transplanted microbiota established by heterologous FMT is not resilient during chemical-induced colonic inflammation, in contrast to that of autologous FMT. Our findings highlighted the need to monitor the long-term stability of transplanted gut microbiota and to perform multiple FMT when necessary.

StrainPanDA: Linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data.

Microbial strains of variable functional capacities coexist in microbiomes. Current bioinformatics methods of strain analysis cannot provide the direct linkage between strain composition and their gene contents from metagenomic data. Here we present Strain-level Pangenome Decomposition Analysis (StrainPanDA), a novel method that uses the pangenome coverage profile of multiple metagenomic samples to simultaneously reconstruct the composition and gene content variation of coexisting strains in microbial communities. We systematically validate the accuracy and robustness of StrainPanDA using synthetic data sets. To demonstrate the power of gene-centric strain profiling, we then apply StrainPanDA to analyze the gut microbiome samples of infants, as well as patients treated with fecal microbiota transplantation. We show that the linked reconstruction of strain composition and gene content profiles is critical for understanding the relationship between microbial adaptation and strain-specific functions (e.g., nutrient utilization and pathogenicity). Finally, StrainPanDA has minimal requirements for computing resources and can be scaled to process multiple species in a community in parallel. In short, StrainPanDA can be applied to metagenomic data sets to detect the association between molecular functions and microbial/host phenotypes to formulate testable hypotheses and gain novel biological insights at the strain or subspecies level.

Drought stress and plant ecotype drive microbiome recruitment in switchgrass rhizosheath.

The rhizosheath, a layer of soil grains that adheres firmly to roots, is beneficial for plant growth and adaptation to drought environments. Switchgrass is a perennial C4 grass which can form contact rhizosheath under drought conditions. In this study, we characterized the microbiomes of four different rhizocompartments of two switchgrass ecotypes (Alamo and Kanlow) grown under drought or well-watered conditions via 16S ribosomal RNA amplicon sequencing. These four rhizocompartments, the bulk soil, rhizosheath soil, rhizoplane, and root endosphere, harbored both distinct and overlapping microbial communities. The root compartments (rhizoplane and root endosphere) displayed low-complexity communities dominated by Proteobacteria and Firmicutes. Compared to bulk soil, Cyanobacteria and Bacteroidetes were selectively enriched, while Proteobacteria and Firmicutes were selectively depleted, in rhizosheath soil. Taxa from Proteobacteria or Firmicutes were specifically selected in Alamo or Kanlow rhizosheath soil. Following drought stress, Citrobacter and Acinetobacter were further enriched in rhizosheath soil, suggesting that rhizosheath microbiome assembly is driven by drought stress. Additionally, the ecotype-specific recruitment of rhizosheath microbiome reveals their differences in drought stress responses. Collectively, these results shed light on rhizosheath microbiome recruitment in switchgrass and lay the foundation for the improvement of drought tolerance in switchgrass by regulating the rhizosheath microbiome.

Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease.

Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

Protein-Inspired Self-Healable Ti3C2 MXenes/Rubber-Based Supramolecular Elastomer for Intelligent Sensing.

Progress toward the integration of electronic sensors with a signal processing system is important for artificial intelligent and smart robotics. It demands mechanically robust, highly sensitive, and self-healable sensing materials which could generate discernible electric variations responding to external stimuli. Here, inspired by the supramolecular interactions of amino acid residues in proteins, we report a self-healable nanostructured Ti3C2MXenes/rubber-based supramolecular elastomer (NMSE) for intelligent sensing. MXene nanoflakes modified with serine through an esterification reaction assemble with an elastomer matrix, constructing delicate dynamic supramolecular hydrogen bonding interfaces. NMSE features desirable recovered toughness (12.34 MJ/m3) and excellent self-healing performance (∼100%) at room temperature. The NMSE-based sensor with high gauge factor (107.43), low strain detection limit (0.1%), and fast responding time (50 ms) can precisely detect subtle human motions (including speech, facial expression, pulse, and heartbeat) and moisture variations even after cut/healing processes. Moreover, NMSE-based sensors integrated with a complete signal process system show great feasibility for speech-controlled motions, which demonstrates promising potential in future wearable electronics and soft intelligent robotics.

[Research progress and applications of strain analysis based on metagenomic data].

Strain is the fundamental unit in microbial taxonomy. The functional diversity among strains has great influence on host phenotypes. With the development of microbiome research, knowing the composition and functional capacities of complex microbial communities at the strain level has become increasingly valuable in scientific research and clinical applications. This review introduces the principles of bioinformatics algorithms for strain analysis based on metagenomic data, the applications in microbiome research and directions of future development.

Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation.

Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions.

Diffuse large B-cell lymphoma patient-derived xenograft models capture the molecular and biological heterogeneity of the disease.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease defined by transcriptional classifications, specific signaling and survival pathways, and multiple low-frequency genetic alterations. Preclinical model systems that capture the genetic and functional heterogeneity of DLBCL are urgently needed. Here, we generated and characterized a panel of large B-cell lymphoma (LBCL) patient-derived xenograft (PDX) models, including 8 that reflect the immunophenotypic, transcriptional, genetic, and functional heterogeneity of primary DLBCL and 1 that is a plasmablastic lymphoma. All LBCL PDX models were subjected to whole-transcriptome sequencing to classify cell of origin and consensus clustering classification (CCC) subtypes. Mutations and chromosomal rearrangements were evaluated by whole-exome sequencing with an extended bait set. Six of the 8 DLBCL models were activated B-cell (ABC)-type tumors that exhibited ABC-associated mutations such as MYD88, CD79B, CARD11, and PIM1. The remaining 2 DLBCL models were germinal B-cell type, with characteristic alterations of GNA13, CREBBP, and EZH2, and chromosomal translocations involving IgH and either BCL2 or MYC Only 25% of the DLBCL PDX models harbored inactivating TP53 mutations, whereas 75% exhibited copy number alterations of TP53 or its upstream modifier, CDKN2A, consistent with the reported incidence and type of p53 pathway alterations in primary DLBCL. By CCC criteria, 6 of 8 DLBCL PDX models were B-cell receptor (BCR)-type tumors that exhibited selective surface immunoglobulin expression and sensitivity to entospletinib, a recently developed spleen tyrosine kinase inhibitor. In summary, we have established and characterized faithful PDX models of DLBCL and demonstrated their usefulness in functional analyses of proximal BCR pathway inhibition.

Targetable genetic features of primary testicular and primary central nervous system lymphomas.

Primary central nervous system lymphomas (PCNSLs) and primary testicular lymphomas (PTLs) are extranodal large B-cell lymphomas (LBCLs) with inferior responses to current empiric treatment regimens. To identify targetable genetic features of PCNSL and PTL, we characterized their recurrent somatic mutations, chromosomal rearrangements, copy number alterations (CNAs), and associated driver genes, and compared these comprehensive genetic signatures to those of diffuse LBCL and primary mediastinal large B-cell lymphoma (PMBL). These studies identify unique combinations of genetic alterations in discrete LBCL subtypes and subtype-selective bases for targeted therapy. PCNSLs and PTLs frequently exhibit genomic instability, and near-uniform, often biallelic, CDKN2A loss with rare TP53 mutations. PCNSLs and PTLs also use multiple genetic mechanisms to target key genes and pathways and exhibit near-uniform oncogenic Toll-like receptor signaling as a result of MYD88 mutation and/or NFKBIZ amplification, frequent concurrent B-cell receptor pathway activation, and deregulation of BCL6. Of great interest, PCNSLs and PTLs also have frequent 9p24.1/PD-L1/PD-L2 CNAs and additional translocations of these loci, structural bases of immune evasion that are shared with PMBL.

SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data.

The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions' background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions' supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.

GlycReSoft: a software package for automated recognition of glycans from LC/MS data.

Glycosylation modifies the physicochemical properties and protein binding functions of glycoconjugates. These modifications are biosynthesized in the endoplasmic reticulum and Golgi apparatus by a series of enzymatic transformations that are under complex control. As a result, mature glycans on a given site are heterogeneous mixtures of glycoforms. This gives rise to a spectrum of adhesive properties that strongly influences interactions with binding partners and resultant biological effects. In order to understand the roles glycosylation plays in normal and disease processes, efficient structural analysis tools are necessary. In the field of glycomics, liquid chromatography/mass spectrometry (LC/MS) is used to profile the glycans present in a given sample. This technology enables comparison of glycan compositions and abundances among different biological samples, i.e. normal versus disease, normal versus mutant, etc. Manual analysis of the glycan profiling LC/MS data is extremely time-consuming and efficient software tools are needed to eliminate this bottleneck. In this work, we have developed a tool to computationally model LC/MS data to enable efficient profiling of glycans. Using LC/MS data deconvoluted by Decon2LS/DeconTools, we built a list of unique neutral masses corresponding to candidate glycan compositions summarized over their various charge states, adducts and range of elution times. Our work aims to provide confident identification of true compounds in complex data sets that are not amenable to manual interpretation. This capability is an essential part of glycomics work flows. We demonstrate this tool, GlycReSoft, using an LC/MS dataset on tissue derived heparan sulfate oligosaccharides. The software, code and a test data set are publically archived under an open source license.