Competition between co-dominant plants of the Serengeti plains depends on competitor identity,water, and urine

Kyllinga nervosa (Steud.) and Sporobolus kentrophyllus (K. Schum.) are co-dominant plants of the Serengeti short-grass plains, Tanzania. The plains are characterized by seasonal and sporadic rainfall and currently support in excess of 1.5 million migratory ungulates. The interactive effect of simulated bovine urine and water availability were tested on the competitive interactions of these species in the laboratory. Sporobolus kentrophyllus was a superior competitor to K. nervosa over the tested treatment levels with respect to growth and reproductive effort. Sporobolus kentrophyllus exhibited rapid growth in response to urine addition, leading to a significant species × urine interaction while reduced growth by K. nervosa in response to low water availability explained the significant species × water interaction and is likely explained by K. nervosa's shallow root system. Kyllinga nervosa, however, appears to be more tolerant of low nitrogen conditions based on its similar growth with and without the urine treatment. The effect of intraspecific competition on total biomass was similar for S. kentrophyllus and K. nervosa. Competition resulted in increased size differences (asymmetry) for K. nervosa and for the interspecific competition treatments compared to the size differences observed for plants grown individually (in absence of competition).Total reproductive biomass was reduced most by competition with S. kentrophyllus, irrespective of target species. The water treatment did not influence reproduction while the urine treatment significantly increased reproductive biomass and interacted with target species, competitor species, and yielded a three-way urine × target × competitor species interaction.Results suggest that codominance of these two species in the Serengeti is regulated by water availability, nitrogen input from grazers, and local neighbor identity.     

EXO5-DNA structure and BLM interactions direct DNA resection critical for ATR-dependent replication restart

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The Growth,physiological and biochemical response of foxtail millet to atrazine herbicide

Foxtail millet (Pennisetum glaucum L.) is a vital crop that is planted as food and fodder crop around the globe. There is only limited information is present for abiotic stresses on the physiological responses to atrazine. A field experiment was conducted to investigate the effects of different atrazine dosages on the growth, fluorescence and physiological parameters i.e., malonaldehyde (MDA) and reactive oxygen species (ROS) (H₂O₂ and O₂) in the leaves to know the extent of atrazine on oxidative damage of foxtail millet. Our experiment consisted of 0, 2.5, 12.5, 22.5 and 32.5 (mg/kg) of labeled atrazine doses on 2 foxtaill millet varieties. High doses of atrazine significantly enhanced ROS and MDA synthesis in the plant leaves. Enzymes activities like ascorbate peroxidase (APX) and peroxidase (POD) activities enhanced, while catalase (CAD) and superoxide dismutase (SOD) activities reduced with increasing atrazine concentrations. Finally atrazine doses at 32.5 mg/kg reduced chlorophyll contents, while chlorophyll (a/b) ratio also enhanced. Biomass, plant height, chlorophyll fluorescence parameters, minimal and maximal fluorescence (Fo, Fm), maximum and actual quantum yield, photochemical quenching coefficient, and electron transport rate are decreased with increasing atrazine doses.     

四川省自然保护区时空分布与影响因素

自然保护区是为保护具有代表性的生态系统和濒危动植物而划分的特定区域,在涵养水土,防风固沙、净化空气、保护生物多样性等方面发挥着重要作用。四川省有自然保护区166处,类型丰富多样,是中国自然保护地体系的重要组成部分,其保护对象涵盖珍稀动植物,保护功能涉及物种、水源和生态环境,与国家地质公园、湿地公园、森林公园等共同维系着中国西南地区,乃至青藏高原东缘的生态系统。因此,研究四川省自然保护区的空间分布格局及其影响因素具有重要的价值和意义。运用地理空间分析方法对1963-2018年间四川省自然保护区的空间分布和影响因素进行了研究。研究发现:①四川省自然保护区空间分布的总体特征以集聚为主,呈现集聚-随机-集聚的变化特征,且前期变化幅度大,后期变化幅度小,总体发展明显分为1963-1998年的单核形成与发展阶段和1998-2018年的双核阶段;②四川省自然保护区主要分布在成都平原向川西高原的过渡区域,其均衡度类型在时间上表现出由"差距悬殊"到"差距较大"的演变特征;③四川省自然保护区的重心活动范围相对较小,基本稳定在阿坝州南部。标准差椭圆的长短半轴和面积均变化强烈,总体呈现出大幅度的增长,空间分布由南-北向演变为东北-西南向;④自然保护区受到自然因素和社会因素的双重影响,高密度区域分布在地势适中、气候温和、河流众多、土壤肥沃、人口稀少的阿坝州南部与东部地区。未来,四川省生态功能建设应该立足国家公园、自然保护区和自然公园的特点、分布状况,对自然保护区分布较少的川西北、川东北和川南部分地区进行优化布局,以加强这些地区的生态功能建设。同时,探索自然保护区的发展模式,实现自然保护区与周边区域社会经济的协调发展。     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Simple But Efficacious Enrichment of Integral Membrane Proteins and Their Interactions for In-Depth Membrane Proteomics

Membrane proteins play essential roles in various cellular processes, such as nutrient transport, bioenergetic processes, cell adhesion, and signal transduction. Proteomics is one of the key approaches to exploring membrane proteins comprehensively. Bottom–up proteomics using LC–MS/MS has been widely used in membrane proteomics. However, the low abundance and hydrophobic features of membrane proteins, especially integral membrane proteins, make it difficult to handle the proteins and are the bottleneck for identification by LC–MS/MS. Herein, to improve the identification and quantification of membrane proteins, we have stepwisely evaluated methods of membrane enrichment for the sample preparation. The enrichment methods of membranes consisted of precipitation by ultracentrifugation and treatment by urea or alkaline solutions. The best enrichment method in the study, washing with urea after isolation of the membranes, resulted in the identification of almost twice as many membrane proteins compared with samples without the enrichment. Notably, the method significantly enhances the identified numbers of multispanning transmembrane proteins, such as solute carrier transporters, ABC transporters, and G-protein–coupled receptors, by almost sixfold. Using this method, we revealed the profiles of amino acid transport systems with the validation by functional assays and found more protein–protein interactions, including membrane protein complexes and clusters. Our protocol uses standard procedures in biochemistry, but the method was efficient for the in-depth analysis of membrane proteome in a wide range of samples.     

Metabolic Enzyme Alterations and Astrocyte Dysfunction in a Murine Model of Alexander Disease With Severe Reactive Gliosis

Alexander disease (AxD) is a rare and fatal neurodegenerative disorder caused by mutations in the gene encoding glial fibrillary acidic protein (GFAP). In this report, a mouse model of AxD (GFAP^Tg;Gfap^+/R236H) was analyzed that contains a heterozygous R236H point mutation in murine Gfap as well as a transgene with a GFAP promoter to overexpress human GFAP. Using label-free quantitative proteomic comparisons of brain tissue from GFAP^Tg;Gfap^+/R236H versus wild-type mice confirmed upregulation of the glutathione metabolism pathway and indicated proteins were elevated in the peroxisome proliferator-activated receptor (PPAR) signaling pathway, which had not been reported previously in AxD. Relative protein-level differences were confirmed by a targeted proteomics assay, including proteins related to astrocytes and oligodendrocytes. Of particular interest was the decreased level of the oligodendrocyte protein, 2-hydroxyacylsphingosine 1-beta-galactosyltransferase (Ugt8), since Ugt8-deficient mice exhibit a phenotype similar to GFAP^Tg;Gfap^+/R236H mice (e.g., tremors, ataxia, hind-limb paralysis). In addition, decreased levels of myelin-associated proteins were found in the GFAP^Tg;Gfap^+/R236H mice, consistent with the role of Ugt8 in myelin synthesis. Fabp7 upregulation in GFAP^Tg;Gfap^+/R236H mice was also selected for further investigation due to its uncharacterized association to AxD, critical function in astrocyte proliferation, and functional ability to inhibit the anti-inflammatory PPAR signaling pathway in models of amyotrophic lateral sclerosis (ALS). Within Gfap⁺ astrocytes, Fabp7 was markedly increased in the hippocampus, a brain region subjected to extensive pathology and chronic reactive gliosis in GFAP^Tg;Gfap^+/R236H mice. Last, to determine whether the findings in GFAP^Tg;Gfap^+/R236H mice are present in the human condition, AxD patient and control samples were analyzed by Western blot, which indicated that Type I AxD patients have a significant fourfold upregulation of FABP7. However, immunohistochemistry analysis showed that UGT8 accumulates in AxD patient subpial brain regions where abundant amounts of Rosenthal fibers are located, which was not observed in the GFAP^Tg;Gfap^+/R236H mice.     

Extending the Horizon of Homology Detection with Coevolution-based Structure Prediction

Traditional sequence analysis algorithms fail to identify distant homologies when they lie beyond a detection horizon. In this review, we discuss how co-evolution-based contact and distance prediction methods are pushing back this homology detection horizon, thereby yielding new functional insights and experimentally testable hypotheses. Based on correlated substitutions, these methods divine three-dimensional constraints among amino acids in protein sequences that were previously devoid of all annotated domains and repeats. The new algorithms discern hidden structure in an otherwise featureless sequence landscape. Their revelatory impact promises to be as profound as the use, by archaeologists, of ground-penetrating radar to discern long-hidden, subterranean structures. As examples of this, we describe how triplicated structures reflecting longin domains in MON1A-like proteins, or UVR-like repeats in DISC1, emerge from their predicted contact and distance maps. These methods also help to resolve structures that do not conform to a “beads-on-a-string” model of protein domains. In one such example, we describe CFAP298 whose ubiquitin-like domain was previously challenging to perceive owing to a large sequence insertion within it. More generally, the new algorithms permit an easier appreciation of domain families and folds whose evolution involved structural insertion or rearrangement. As we exemplify with α1-antitrypsin, coevolution-based predicted contacts may also yield insights into protein dynamics and conformational change. This new combination of structure prediction (using innovative co-evolution based methods) and homology inference (using more traditional sequence analysis approaches) shows great promise for bringing into view a sea of evolutionary relationships that had hitherto lain far beyond the horizon of homology detection.