The effect of mucA allele on biofilm architecture and the biofilm-related proteomes

In this study, a unique mucA mutation (designated mucA56) was introduced, which was characterized by deletion of bases 166-333, encoding MucA56 protein with the deletion of the trans-membrane region, which then was proved to be cytoplasmic with phoA-mucA fusion method. PAOmucA56 was constructed with homologous recombination; two PAO1 derivatives PAOmucA22 (PDO300) and PAOmucA56 displayed mucoid phenotype on pseudomonas isolation agar (PIA) agar, but PDO300 produced more alginate than PAOmucA56. Scanning confocal laser microscopy was used to observe the biofilm structures of the three strains during various biofilm development stages. PDO300 developed biofilm with low substratum coverage and high structural heterogeneity, while PAOmucA56 and PAO1 formed uniform biofilm with complete substratum coverage. The proteomes of crude protein extracts of biofilm cells revealed that there are 17 candidate proteins differentially expressed between the two kinds of biofilm, which were proteins involved in protein synthesis, MucA degradation, energy metabolism, carbon catabolism and amino acid metabolism and so on. We might conclude that alginate production may affect biofilm architecture, and proteins involved in protein synthesis, MucA degradation, energy metabolism, carbon catabolism and amino acid metabolism might play a role in biofilm development alternatively

Efficient Black-box Checking of Snapshot Isolation in Databases

Snapshot isolation (SI) is a prevalent weak isolation level that avoids the performance penalty imposed by serializability and simultaneously prevents various undesired data anomalies. Nevertheless, SI anomalies have recently been found in production cloud databases that claim to provide the SI guarantee. Given the complex and often unavailable internals of such databases, a black-box SI checker is highly desirable. In this paper we present PolySI, a novel black-box checker that efficiently checks SI and provides understandable counterexamples upon detecting violations. PolySI builds on a novel characterization of SI using generalized polygraphs (GPs), for which we establish its soundness and completeness. PolySI employs an SMT solver and also accelerates SMT solving by utilizing the compact constraint encoding of GPs and domain-specific optimizations for pruning constraints. As demonstrated by our extensive assessment, PolySI successfully reproduces all of 2477 known SI anomalies, detects novel SI violations in three production cloud databases, identifies their causes, outperforms the state-of-the-art black-box checkers under a wide range of workloads, and can scale up to large-sized workloads.Comment: 20 pages, 15 figures, accepted by PVLD

SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is learned indexes, which use a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04x compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+.Comment: Accepted by Conference SIGMOD 24, June 09-15, 2024, Santiago, Chil

Causal relationship between gut microbiota and diabetic nephropathy: a two-sample Mendelian randomization study

ObjectiveEmerging evidence has provided compelling evidence linking gut microbiota (GM) and diabetic nephropathy (DN) via the “gut-kidney” axis. But the causal relationship between them hasn’t been clarified yet. We perform a Two-Sample Mendelian randomization (MR) analysis to reveal the causal connection with GM and the development of DN, type 1 diabetes nephropathy (T1DN), type 2 diabetes nephropathy (T2DN), type 1 diabetes mellitus (T1DM), and type 2 diabetes mellitus (T2DM).MethodsWe used summary data from MiBioGen on 211 GM taxa in 18340 participants. Generalized MR analysis methods were conducted to estimate their causality on risk of DN, T1DN, T2DN, T1DM and T2DM from FinnGen. To ensure the reliability of the findings, a comprehensive set of sensitivity analyses were conducted to confirm the resilience and consistency of the results.ResultsIt was showed that Class Verrucomicrobiae [odds ratio (OR) =1.5651, 95%CI:1.1810-2.0742,PFDR=0.0018], Order Verrucomicrobiales (OR=1.5651, 95%CI: 1.1810-2.0742, PFDR=0.0018) and Family Verrucomicrobiaceae (OR=1.3956, 95%CI:1.0336-1.8844, PFDR=0.0296) had significant risk of DN. Our analysis found significant associations between GM and T2DN, including Class Verrucomimicrobiae (OR=1.8227, 95% CI: 1.2414-2.6763, PFDR=0.0139), Order Verrucomimicrobiae (OR=1.5651, 95% CI: 1.8227-2.6764, PFDR=0.0024), Rhodospirillales (OR=1.8226, 95% CI: 1.2412-2.6763, PFDR=0.0026), and Family Verrucomicroniaceae (OR=1.8226, 95% CI: 1.2412-2.6763, PFDR=0.0083). The Eubacteriumprotogenes (OR=0.4076, 95% CI: 0.2415-0.6882, PFDR=0.0021) exhibited a protection against T1DN. Sensitivity analyses confirmed that there was no significant heterogeneity and pleiotropy.ConclusionsAt the gene prediction level, we identified the specific GM that is causally linked to DN in both T1DM and T2DM patients. Moreover, we identified distinct microbial changes in T1DN that differed from those seen in T2DN, offering valuable insights into GM signatures associated with subtype of nephropathy

Differences in Phenotypic Plasticity between Invasive and Native Plants Responding to Three Environmental Factors

The phenotypic plasticity hypothesis suggests that exotic plants may have greater phenotypic plasticity than native plants. However, whether phenotypic changes vary according to different environmental factors has not been well studied. We conducted a multi-species greenhouse experiment to study the responses of six different phenotypic traits, namely height, leaf number, specific leaf area, total biomass, root mass fraction, and leaf mass fraction, of native and invasive species to nutrients, water, and light. Each treatment was divided into two levels: high and low. In the nutrient addition experiment, only the leaf mass fraction and root mass fraction of the plants supported the phenotypic plasticity hypothesis. Then, none of the six traits supported the phenotypic plasticity hypothesis in the water or light treatment experiments. The results show that, for different environmental factors and phenotypes, the phenotypic plasticity hypothesis of plant invasion is inconsistent. When using the phenotypic plasticity hypothesis to explain plant invasion, variations in environmental factors and phenotypes should be considered

DataSheet_1_Causal relationship between gut microbiota and diabetic nephropathy: a two-sample Mendelian randomization study.pdf

ObjectiveEmerging evidence has provided compelling evidence linking gut microbiota (GM) and diabetic nephropathy (DN) via the “gut-kidney” axis. But the causal relationship between them hasn’t been clarified yet. We perform a Two-Sample Mendelian randomization (MR) analysis to reveal the causal connection with GM and the development of DN, type 1 diabetes nephropathy (T1DN), type 2 diabetes nephropathy (T2DN), type 1 diabetes mellitus (T1DM), and type 2 diabetes mellitus (T2DM).MethodsWe used summary data from MiBioGen on 211 GM taxa in 18340 participants. Generalized MR analysis methods were conducted to estimate their causality on risk of DN, T1DN, T2DN, T1DM and T2DM from FinnGen. To ensure the reliability of the findings, a comprehensive set of sensitivity analyses were conducted to confirm the resilience and consistency of the results.ResultsIt was showed that Class Verrucomicrobiae [odds ratio (OR) =1.5651, 95%CI:1.1810-2.0742,PFDR=0.0018], Order Verrucomicrobiales (OR=1.5651, 95%CI: 1.1810-2.0742, PFDR=0.0018) and Family Verrucomicrobiaceae (OR=1.3956, 95%CI:1.0336-1.8844, PFDR=0.0296) had significant risk of DN. Our analysis found significant associations between GM and T2DN, including Class Verrucomimicrobiae (OR=1.8227, 95% CI: 1.2414-2.6763, PFDR=0.0139), Order Verrucomimicrobiae (OR=1.5651, 95% CI: 1.8227-2.6764, PFDR=0.0024), Rhodospirillales (OR=1.8226, 95% CI: 1.2412-2.6763, PFDR=0.0026), and Family Verrucomicroniaceae (OR=1.8226, 95% CI: 1.2412-2.6763, PFDR=0.0083). The Eubacteriumprotogenes (OR=0.4076, 95% CI: 0.2415-0.6882, PFDR=0.0021) exhibited a protection against T1DN. Sensitivity analyses confirmed that there was no significant heterogeneity and pleiotropy.ConclusionsAt the gene prediction level, we identified the specific GM that is causally linked to DN in both T1DM and T2DM patients. Moreover, we identified distinct microbial changes in T1DN that differed from those seen in T2DN, offering valuable insights into GM signatures associated with subtype of nephropathy. diabetic nephropathy, gut microbiota, type 2 diabetic mellitus, type 1 diabetic mellitus, Mendelian randomization, gut-kidney axis.</p

Efficient Black-box Checking of Snapshot Isolation in Databases

Snapshot isolation (SI) is a prevalent weak isolation level that avoids the performance penalty imposed by serializability and simultaneously prevents various undesired data anomalies. Nevertheless, SI anomalies have recently been found in production cloud databases that claim to provide the SI guarantee. Given the complex and often unavailable internals of such databases, a black-box SI checker is highly desirable. In this paper we present PolySI, a black-box checker that efficiently checks SI and provides understandable counterexamples upon detecting violations. PolySI builds on a characterization of SI using generalized polygraphs (GPs), for which we establish its soundness and completeness. PolySI employs an SMT solver and also accelerates SMT solving by utilizing a compact constraint encoding of GPs and domain-specific optimizations for pruning constraints. As our extensive assessment demonstrates, PolySI successfully reproduces all of 2477 known SI anomalies, detects novel SI violations in three production cloud databases, identifies their causes, outperforms the state-of-the-art black-box checkers under a wide range of workloads, and can scale up to large workloads.ISSN:2150-809

Table_1_Causal relationship between gut microbiota and diabetic nephropathy: a two-sample Mendelian randomization study.xlsx

ObjectiveEmerging evidence has provided compelling evidence linking gut microbiota (GM) and diabetic nephropathy (DN) via the “gut-kidney” axis. But the causal relationship between them hasn’t been clarified yet. We perform a Two-Sample Mendelian randomization (MR) analysis to reveal the causal connection with GM and the development of DN, type 1 diabetes nephropathy (T1DN), type 2 diabetes nephropathy (T2DN), type 1 diabetes mellitus (T1DM), and type 2 diabetes mellitus (T2DM).MethodsWe used summary data from MiBioGen on 211 GM taxa in 18340 participants. Generalized MR analysis methods were conducted to estimate their causality on risk of DN, T1DN, T2DN, T1DM and T2DM from FinnGen. To ensure the reliability of the findings, a comprehensive set of sensitivity analyses were conducted to confirm the resilience and consistency of the results.ResultsIt was showed that Class Verrucomicrobiae [odds ratio (OR) =1.5651, 95%CI:1.1810-2.0742,PFDR=0.0018], Order Verrucomicrobiales (OR=1.5651, 95%CI: 1.1810-2.0742, PFDR=0.0018) and Family Verrucomicrobiaceae (OR=1.3956, 95%CI:1.0336-1.8844, PFDR=0.0296) had significant risk of DN. Our analysis found significant associations between GM and T2DN, including Class Verrucomimicrobiae (OR=1.8227, 95% CI: 1.2414-2.6763, PFDR=0.0139), Order Verrucomimicrobiae (OR=1.5651, 95% CI: 1.8227-2.6764, PFDR=0.0024), Rhodospirillales (OR=1.8226, 95% CI: 1.2412-2.6763, PFDR=0.0026), and Family Verrucomicroniaceae (OR=1.8226, 95% CI: 1.2412-2.6763, PFDR=0.0083). The Eubacteriumprotogenes (OR=0.4076, 95% CI: 0.2415-0.6882, PFDR=0.0021) exhibited a protection against T1DN. Sensitivity analyses confirmed that there was no significant heterogeneity and pleiotropy.ConclusionsAt the gene prediction level, we identified the specific GM that is causally linked to DN in both T1DM and T2DM patients. Moreover, we identified distinct microbial changes in T1DN that differed from those seen in T2DN, offering valuable insights into GM signatures associated with subtype of nephropathy. diabetic nephropathy, gut microbiota, type 2 diabetic mellitus, type 1 diabetic mellitus, Mendelian randomization, gut-kidney axis.</p

Data from: Effects of nitrogen addition and mowing on rodent damage in an Inner Mongolian steppe

Rodent damage is a serious threat to sustainable management of grassland. The effects of nitrogen (N) deposition and grassland management on rodent damage have been scarcely studied. Here, we reported the effects of two years of N addition and mowing on burrow density and damage area of Citellus dauricus in a semi-arid steppe in Inner Mongolia. N addition significantly aggravated, while mowing alleviated rodent damage in the grassland under study. Burrow density and damage area increased 2.8- and 4.7-fold, in N addition plots compared to the ambient N addition treatment, respectively. Conversely, mowing decreased burrow density and damage area by 75.9% and 14.5%, respectively, compared to no mowing plots.. Observed changes in rodent damage were mainly due to variations in plant community cover, height, and aboveground net primary productivity. Our findings demonstrate that N addition and mowing can affect the rodent density and activity in grassland, suggesting that the effects of a changing atmospheric composition and land use on rodent damage must be considered in order to achieve better grassland management

Image_2_Mendelian randomization study supports the causal association between serum cystatin C and risk of diabetic nephropathy.jpeg

AimsCystatin C, an inhibitor of cysteine protease, has been used as a biomarker for estimating glomerular filtration rate. However, the causal relation between cystatin C and diabetic nephropathy remains uncertain.MethodsWe assessed the causal effect of cystatin C together with other five serum biomarkers including KIM-1, GDF-15, TBIL, uric acid, and Scr on diabetic nephropathy by Mendelian randomization (MR) analysis. 234 genetic variants were selected as instrumental variables to evaluate the causal effect of cystatin C (NGWAS=361194) on diabetic nephropathy (Ncase/Ncontrol up to 3283/210463). Multivariable MR (MVMR) was performed to assess the stability of cystatin C’s causal relationship. Two-step MR was used to assess the mediation effect of BMI and SBP.ResultsAmong the six serum biomarkers, only cystatin C causally associated with diabetic nephropathy (IVW OR: 1.36, 95%CI [1.15, 1.61]). After adjusting for the potential confounders BMI and SBP, cystatin C maintained its causal effect on the DN (OR: 1.17, 95%CI [1.02, 1.33]), which means that the risk of DN increased by 17% with an approximate 1 standard deviation (SD) increment of serum cystatin C level. Two-step MR results indicated that BMI might mediate the causal effect of cystatin C on diabetic nephropathy.InterpretationOur findings discovered that cystatin C was a risk factor for diabetic nephropathy independent of BMI and SBP in diabetes mellitus patients. Future research is required to illustrate the underlying mechanism and prove targeting circulating cystatin C could be a potential therapy method.</p