Environmentally dependent interactions shape patterns in gene content across natural microbiomes.

Sequencing surveys of microbial communities in hosts, oceans and soils have revealed ubiquitous patterns linking community composition to environmental conditions. While metabolic capabilities restrict the environments suitable for growth, the influence of ecological interactions on patterns observed in natural microbiomes remains uncertain. Here we use denitrification as a model system to demonstrate how metagenomic patterns in soil microbiomes can emerge from pH-dependent interactions. In an analysis of a global soil sequencing survey, we find that the abundances of two genotypes trade off with pH; nar gene abundances increase while nap abundances decrease with declining pH. We then show that in acidic conditions strains possessing nar fail to grow in isolation but are enriched in the community due to an ecological interaction with nap genotypes. Our study provides a road map for dissecting how associations between environmental variables and gene abundances arise from environmentally modulated community interactions.

Spatiotemporal constrained RNA-protein heterogeneous network for protein complex identification.

The identification of protein complexes from protein interaction networks is crucial in the understanding of protein function, cellular processes and disease mechanisms. Existing methods commonly rely on the assumption that protein interaction networks are highly reliable, yet in reality, there is considerable noise in the data. In addition, these methods fail to account for the regulatory roles of biomolecules during the formation of protein complexes, which is crucial for understanding the generation of protein interactions. To this end, we propose a SpatioTemporal constrained RNA-protein heterogeneous network for Protein Complex Identification (STRPCI). STRPCI first constructs a multiplex heterogeneous protein information network to capture deep semantic information by extracting spatiotemporal interaction patterns. Then, it utilizes a dual-view aggregator to aggregate heterogeneous neighbor information from different layers. Finally, through contrastive learning, STRPCI collaboratively optimizes the protein embedding representations under different spatiotemporal interaction patterns. Based on the protein embedding similarity, STRPCI reweights the protein interaction network and identifies protein complexes with core-attachment strategy. By considering the spatiotemporal constraints and biomolecular regulatory factors of protein interactions, STRPCI measures the tightness of interactions, thus mitigating the impact of noisy data on complex identification. Evaluation results on four real PPI networks demonstrate the effectiveness and strong biological significance of STRPCI. The source code implementation of STRPCI is available from https://github.com/LI-jasm/STRPCI.

Temporal Protein Complex Identification Based on Dynamic Heterogeneous Protein Information Network Representation Learning.

Protein complexes, as the fundamental units of cellular function and regulation, play a crucial role in understanding the normal physiological functions of cells. Existing methods for protein complex identification attempt to introduce other biological information on top of the protein-protein interaction (PPI) network to assist in evaluating the degree of association between proteins. However, these methods usually treat protein interaction networks as flat homogeneous static networks. They cannot distinguish the roles and importance of different types of biological information, nor can they reflect the dynamic changes of protein complexes. In recent years, heterogeneous network representation learning has achieved great success in processing complex heterogeneous information and mining deep semantics. We thus propose a temporal protein complex identification method based on Dynamic Heterogeneous Protein information network Representation Learning, DHPRL. DHPRL naturally integrates multiple types of heterogeneous biological information in the cellular temporal dimension. It simultaneously models the temporal dynamic properties of proteins and the heterogeneity of biological information to improve the understanding of protein interactions and the accuracy of complex prediction. Firstly, we construct Dynamic Heterogeneous Protein Information Network (DHPIN) by integrating temporal gene expression information and GO attribute information. Then we design a dual-view collaborative contrast mechanism. Specifically, proposing to learn protein representations from two views of DHPIN (1-hop relation view and meta-path view) to model the consistency and specificity between nearest-neighbour bio information and deeper biological semantics. The dynamic PPI network is thereafter re-weighted based on the learned protein representations. Finally, we perform protein identification on the re-weighted dynamic PPI network. Extensive experimental results demonstrate that DHPRL can effectively model complicated biological information and achieve state-of-the-art performance in most cases. The source code and datasets for DHPR are available at https://github.com/LI-jasm/DHPRL.

Statistical prediction of microbial metabolic traits from genomes.

The metabolic activity of microbial communities is central to their role in biogeochemical cycles, human health, and biotechnology. Despite the abundance of sequencing data characterizing these consortia, it remains a serious challenge to predict microbial metabolic traits from sequencing data alone. Here we culture 96 bacterial isolates individually and assay their ability to grow on 10 distinct compounds as a sole carbon source. Using these data as well as two existing datasets, we show that statistical approaches can accurately predict bacterial carbon utilization traits from genomes. First, we show that classifiers trained on gene content can accurately predict bacterial carbon utilization phenotypes by encoding phylogenetic information. These models substantially outperform predictions made by constraint-based metabolic models automatically constructed from genomes. This result solidifies our current knowledge about the strong connection between phylogeny and metabolic traits. However, phylogeny-based predictions fail to predict traits for taxa that are phylogenetically distant from any strains in the training set. To overcome this we train improved models on gene presence/absence to predict carbon utilization traits from gene content. We show that models that predict carbon utilization traits from gene presence/absence can generalize to taxa that are phylogenetically distant from the training set either by exploiting biochemical information for feature selection or by having sufficiently large datasets. In the latter case, we provide evidence that a statistical approach can identify putatively mechanistic genes involved in metabolic traits. Our study demonstrates the potential power for predicting microbial phenotypes from genotypes using statistical approaches.

Semantic-enhanced Graph Contrastive Learning with Adaptive Denoising for Drug Repositioning.

The traditional drug development process requires a significant investment in workforce and financial resources. Drug repositioning as an efficient alternative has attracted much attention during the last few years. Despite the wide application and success of the method, there are still many shortcomings in the existing model. For example, sparse datasets will seriously affect the existing methods' performance. Additionally, these methods do not pay attention to the noise in datasets. In response to the above defects, we propose a semantic-enriched augmented graph contrastive learning with an adaptive denoising method, called SGCD. This method enhances data from the perspective of the embedding layer, deeply mines potential neighborhood relation-ships in semantic space, and combines similar drugs in the semantic neighborhoods into prototype comparison targets, thus effectively mitigating the impact of data sparsity on the model. Moreover, to enhance the model's robustness to noisy data, we use the adaptive denoising method, which can effectively identify noisy data in the training process. Exhaustive experiments on multiple real datasets show the effectiveness of the proposed model. The code implementation is available at https://github.com/yuhuimin11/SGCD-master.

Global patterns in gene content of soil microbiomes emerge from microbial interactions.

Microbial metabolism sustains life on Earth. Sequencing surveys of communities in hosts, oceans, and soils have revealed ubiquitous patterns linking the microbes present, the genes they possess, and local environmental conditions. One prominent explanation for these patterns is environmental filtering: local conditions select strains with particular traits. However, filtering assumes ecological interactions do not influence patterns, despite the fact that interactions can and do play an important role in structuring communities. Here, we demonstrate the insufficiency of the environmental filtering hypothesis for explaining global patterns in topsoil microbiomes. Using denitrification as a model system, we find that the abundances of two characteristic genotypes trade-off with pH; nar gene abundances increase while nap abundances decrease with declining pH. Contradicting the filtering hypothesis, we show that strains possessing the Nar genotype are enriched in low pH conditions but fail to grow alone. Instead, the dominance of Nar genotypes at low pH arises from an ecological interaction with Nap genotypes that alleviates nitrite toxicity. Our study provides a roadmap for dissecting how global associations between environmental variables and gene abundances arise from environmentally modulated community interactions.

Protein Complex Identification Based on Heterogeneous Protein Information Network.

Protein complexes are the foundation of all cellular activities, and accurately identifying them is crucial for studying cellular systems. The efficient discovery of protein complexes is a focus of research in the field of bioinformatics. Most existing methods for protein complex identification are based on the structure of the protein-protein interaction (PPI) network, whereas some methods attempt to integrate biological information to enhance the features of the protein network for complex identification. Existing protein complex identification methods are unable to fully integrate network topology information and biological attribute information. Most of these methods are based on homogeneous networks and cannot distinguish the importance of different attributes and protein nodes. To address these issues, a GO attribute Heterogeneous Attention network Embedding (GHAE) method based on heterogeneous protein information networks is proposed. First, GHAE incorporates Gene Ontology (GO) information into the PPI network, constructing a heterogeneous protein information network. Then, GHAE uses a dual attention mechanism and heterogeneous graph convolutional representation learning method to learn protein features and to identify protein complexes. The experimental results show that building heterogeneous protein information networks can fully integrate valuable biological information. The heterogeneous graph embedding learning method can simultaneously mine the features of protein and GO attributes, thereby improving the performance of protein complex identification.

Wetland Park Planning and Management Based on the Valuation of Ecosystem Services: A Case Study of the Tieling Lotus Lake National Wetland Park (LLNWP), China.

The valuation of wetland ecosystem services and the construction of environmental landscapes are generally recognized as contributing to the sustainable development of human wellbeing. The valuation of ecosystem services plays an important role in planning for the recovery of degraded wetlands and in urban wetland park management; however, the role of the valuation of ecosystem services is always ignored. To bring more intuitive awareness to the importance of the ecological functions of wetlands and to rationally plan wetland parks, the Lotus Lake National Wetland Park (LLNWP), an urban wetland park in Northeast China, was selected as the study area. We referred to the millennium ecosystem assessment (MA) method and calculated the valuation of this park using the market value, benefit transfer, shadow engineering, carbon tax, and travel cost. ArcGIS was used for remote sensing interpretation. The research results were as follows. LLNWP was classified under seven types of land-use. The functions of the ecosystem services included provisioning, regulating, supporting, and cultural services, and their total value in LLNWP was 11.68×108 CNY. Regarding the per-unit area value of the ecological service functions of different land types, it was found that forest swamp > herbaceous swamp > artificial wetland > permanent river > floodplain wetland. Combined with the characteristics of the functions of its ecosystem's services, LLNWP was divided into ecological and socio-cultural functions. Then, according to the main service functions of the different land types, we propose that the space in LLNWP can be reused, and proposal planning and management suggestions can be made with the aim of preserving the basic functions.

Closed microbial communities self-organize to persistently cycle carbon.

Cycles of nutrients (N, P, etc.) and resources (C) are a defining emergent feature of ecosystems. Cycling plays a critical role in determining ecosystem structure at all scales, from microbial communities to the entire biosphere. Stable cycles are essential for ecosystem persistence because they allow resources and nutrients to be regenerated. Therefore, a central problem in ecology is understanding how ecosystems are organized to sustain robust cycles. Addressing this problem quantitatively has proved challenging because of the difficulties associated with manipulating ecosystem structure while measuring cycling. We address this problem using closed microbial ecosystems (CES), hermetically sealed microbial consortia provided with only light. We develop a technique for quantifying carbon cycling in hermetically sealed microbial communities and show that CES composed of an alga and diverse bacterial consortia self-organize to robustly cycle carbon for months. Comparing replicates of diverse CES, we find that carbon cycling does not depend strongly on the taxonomy of the bacteria present. Moreover, despite strong taxonomic differences, self-organized CES exhibit a conserved set of metabolic capabilities. Therefore, an emergent carbon cycle enforces metabolic but not taxonomic constraints on ecosystem organization. Our study helps establish closed microbial communities as model ecosystems to study emergent function and persistence in replicate systems while controlling community composition and the environment.

Enantioselective total synthesis of (-)-limaspermidine and formal synthesis of (-)-1-acetylaspidoalbidine.

Evolution of the synthetic strategy that culminated in the first asymmetric total synthesis of the Aspidosperma alkaloid limaspermidine is described. The successful enantioselective route to (-)-limaspermidine proceeds in 10 steps and with the isolation of only six intermediates using a Pd-catalyzed enantioselective decarboxylative allylation we have recently developed. This first enantioselective synthesis of (-)-limaspermidine establishes unambiguously its absolute configuration and allows the first asymmetric formal total synthesis of the Aspidoalbine alkaloid (-)-1-acetylaspidoalbidine.

Enantioselective conjugate addition of both aromatic ketones and acetone to nitroolefins catalyzed by chiral primary amines bearing multiple hydrogen-bonding donors.

A new class of chiral primary amine catalysts bearing multiple hydrogen-bonding donors have been designed and synthesized. The newly developed bifunctional organocatalysts efficiently catalyzed not only enantioselective conjugate addition of aromatic ketones to nitroolefins in good yields (up to 87%) with excellent enantioselectivities (97→99% ee) but also enantioselective conjugate addition of acetone to nitroolefins in excellent yields (90-96%) with high enantioselectivities (up to 97% ee).

Highly enantioselective one-pot synthesis of spirocyclopentaneoxindoles containing the oxime group by organocatalyzed Michael addition/ISOC/fragmentation sequence.

A highly diastereo- and enantioselective organocatalytic protocol for the synthesis of biologically important spirocyclopentaneoxindoles containing the oxime functional group from easily accessible 3-allyl-substituted oxindoles and nitroolefins has been developed by a one-pot Michael addition/ISOC/fragmentation sequence.

Catalytic asymmetric construction of spirocyclopentaneoxindoles by a combined Ru-catalyzed cross-metathesis/double Michael addition sequence.

Biologically important and synthetically challenging spirocyclopentaneoxindoles with four contiguous stereocenters including one spiroquaternary stereocenter have been constructed in good yields (72-87%) with excellent diastereoselectivity (16:1→30:1 dr) and enantioselectivity (93→99% ee) by a combined Ru-catalyzed cross-metathesis/organocatalyzed asymmetric double-Michael addition sequence.