Identifying Epistasis in Cancer Genomes: A Delicate Affair.

Recent studies of the tumor genome seek to identify cancer pathways as groups of genes in which mutations are epistatic with one another or, specifically, "mutually exclusive." Here, we show that most mutations are mutually exclusive not due to pathway structure but to interactions with disease subtype and tumor mutation load. In particular, many cancer driver genes are mutated preferentially in tumors with few mutations overall, causing mutations in these cancer genes to appear mutually exclusive with numerous others. Researchers should view current epistasis maps with caution until we better understand the multiple cause-and-effect relationships among factors such as tumor subtype, positive selection for mutations, and gross tumor characteristics including mutational signatures and load.

WSGMB: weight signed graph neural network for microbial biomarker identification.

The stability of the gut microenvironment is inextricably linked to human health, with the onset of many diseases accompanied by dysbiosis of the gut microbiota. It has been reported that there are differences in the microbial community composition between patients and healthy individuals, and many microbes are considered potential biomarkers. Accurately identifying these biomarkers can lead to more precise and reliable clinical decision-making. To improve the accuracy of microbial biomarker identification, this study introduces WSGMB, a computational framework that uses the relative abundance of microbial taxa and health status as inputs. This method has two main contributions: (1) viewing the microbial co-occurrence network as a weighted signed graph and applying graph convolutional neural network techniques for graph classification; (2) designing a new architecture to compute the role transitions of each microbial taxon between health and disease networks, thereby identifying disease-related microbial biomarkers. The weighted signed graph neural network enhances the quality of graph embeddings; quantifying the importance of microbes in different co-occurrence networks better identifies those microbes critical to health. Microbes are ranked according to their importance change scores, and when this score exceeds a set threshold, the microbe is considered a biomarker. This framework's identification performance is validated by comparing the biomarkers identified by WSGMB with actual microbial biomarkers associated with specific diseases from public literature databases. The study tests the proposed computational framework using actual microbial community data from colorectal cancer and Crohn's disease samples. It compares it with the most advanced microbial biomarker identification methods. The results show that the WSGMB method outperforms similar approaches in the accuracy of microbial biomarker identification.

Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA).

Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data.

Potential of focal cortical dysplasia in migraine pathogenesis.

Focal cortical dysplasias are abnormalities of the cerebral cortex associated with an elevated risk of neurological disturbances. Cortical spreading depolarization/depression is a correlate of migraine aura/headache and a trigger of migraine pain mechanisms. However, cortical spreading depolarization/depression is associated with cortical structural changes, which can be classified as transient focal cortical dysplasias. Migraine is reported to be associated with changes in various brain structures, including malformations and lesions in the cortex. Such malformations may be related to focal cortical dysplasias, which may play a role in migraine pathogenesis. Results obtained so far suggest that focal cortical dysplasias may belong to the causes and consequences of migraine. Certain focal cortical dysplasias may lower the threshold of cortical excitability and facilitate the action of migraine triggers. Migraine prevalence in epileptic patients is higher than in the general population, and focal cortical dysplasias are an established element of epilepsy pathogenesis. In this narrative/hypothesis review, we present mainly information on cortical structural changes in migraine, but studies on structural alterations in deep white matter and other brain regions are also presented. We develop the hypothesis that focal cortical dysplasias may be causally associated with migraine and link pathogeneses of migraine and epilepsy.

A comparative analysis of mutual information methods for pairwise relationship detection in metagenomic data.

BACKGROUND: Construction of co-occurrence networks in metagenomic data often employs correlation to infer pairwise relationships between microbes. However, biological systems are complex and often display qualities non-linear in nature. Therefore, the reliance on correlation alone may overlook important relationships and fail to capture the full breadth of intricacies presented in underlying interaction networks. It is of interest to incorporate metrics that are not only robust in detecting linear relationships, but non-linear ones as well. RESULTS: In this paper, we explore the use of various mutual information (MI) estimation approaches for quantifying pairwise relationships in biological data and compare their performances against two traditional measures-Pearson's correlation coefficient, r, and Spearman's rank correlation coefficient, ρ. Metrics are tested on both simulated data designed to mimic pairwise relationships that may be found in ecological systems and real data from a previous study on C. diff infection. The results demonstrate that, in the case of asymmetric relationships, mutual information estimators can provide better detection ability than Pearson's or Spearman's correlation coefficients. Specifically, we find that these estimators have elevated performances in the detection of exploitative relationships, demonstrating the potential benefit of including them in future metagenomic studies. CONCLUSIONS: Mutual information (MI) can uncover complex pairwise relationships in biological data that may be missed by traditional measures of association. The inclusion of such relationships when constructing co-occurrence networks can result in a more comprehensive analysis than the use of correlation alone.

The application of varying amount of green manure combined with nitrogen fertilizer altered the soil bacterial community and rice yield in karst paddy areas.

Long-term application of green manure (GM) and nitrogen (N) fertilizers markedly improved soil fertility and boosted rice yield in ecologically fragile karst paddy fields. However, the precise response mechanisms of the soil bacterial community to varying amounts of green manure alone and in combination with N fertilizer in such environments remain poorly elucidated. In this study, we investigated the soil bacterial communities, keystone taxa, and their relationship with soil environmental variables across eight fertilization treatments. These treatments included group without N addition (N0M0, no N fertilizer and no GM; N0M22.5, 22.5 t/ha GM; N0M45, 45 t/ha GM, N0M67.5, 67.5 t/ha GM) and group with N addition (NM0, N fertilizer and no GM; NM22.5, N fertilizer and 22.5 t/ha GM; NM45, N fertilizer and 45 t/ha GM; NM67.5, N fertilizer and 67.5 t/ha GM). The results revealed that increasing green manure input significantly boosted rice yield by 15.51-22.08% and 21.84-35% in both the group without and with N addition, respectively, compared to N0M0 treatment. Moreover, with escalating green manure input, soil TN, AN, AK, and AP showed an increasing trend in the group without N addition. However, following the addition of N fertilizer, TN and AN content initially rose, followed by a decline due to the enhanced nutrient availability for rice. Furthermore, the application of a large amount of N fertilizer decreased the C: N ratio in the soil, resulting in significant changes in both the soil microbial community and its function. Particularly noteworthy was the transition of keystone taxa from their original roles as N-fixing and carbon-degrading groups (oligotrophs) to roles in carbon degradation (copiotrophs), nitrification, and denitrification. This shift in soil community and function might serve as a primary factor contributing to enhanced nutrient utilization efficiency in rice, thus significantly promoting rice yield.

Interactions of rhizosphere microbiota-environmental factors-pharmacological active ingredients of Eucommia ulmoides.

MAIN CONCLUSION: The composition, diversity and co-occurrence patterns of the rhizosphere microbiota of E. ulmoides were significantly influenced by environmental factors, and which were potentially associated with the contents of pharmacological active ingredients. Eucommia ulmoides is an important perennial medicinal plant. However, little is known about the interactions among microbiota, environmental factors (EFs), and pharmacological active ingredients (PAIs) of E. ulmoides. Herein, we analyzed the interactions among rhizosphere microbiota-EFs-PAIs of E. ulmoides by amplicon sequencing and multi-analytical approach. Our results revealed variations in the dominant genera, diversity, and co-occurrence networks of the rhizosphere microbiota of E. ulmoides across different geographical locations. Notably, available nitrogen exerted the strongest influence on fungal dominant genera, while pH significantly impacted bacterial dominant genera. Rainfall and relative humidity exhibited pronounced effects on the α-diversity of fungal groups, whereas available phosphorus influenced the number of nodes in fungal co-occurrence networks. Altitude and total phosphorus had substantial effects on the average degree and nodes in bacterial co-occurrence networks. Furthermore, the dominant genera, diversity and co-occurrence network of rhizosphere microbiota of E. ulmoides were significantly correlated with the content of PAIs. Specifically, the abundance of rhizosphere dominant genera Filobasidium, Hannaella and Nitrospira were significantly correlated with the content of pinoresinol diglucoside (PD). Similarly, the abundance of Vishniacozyma and Bradyrhizobium correlated significantly with the content of geniposidic acid (GC), while the abundance of Gemmatimonas was significantly correlated with the content of aucubin. Moreover, the bacterial co-occurrence network parameters including average degree, density, and edge, were significantly correlated with the content of GC and aucubin. The α-diversity index Chao1 also displayed a significant correlation with the content of PD. These findings contribute to a more comprehensive understanding of the interactions between medicinal plants and microbes.

Bacterial community structure and co-occurrence networks in the rhizosphere and root endosphere of the grafted apple.

BACKGROUND: Compared with aerial plant tissues (such as leaf, stem, and flower), root-associated microbiomes play an indisputable role in promoting plant health and productivity. We thus explored the similarities and differences between rhizosphere and root endosphere bacterial community in the grafted apple system. RESULTS: Using pot experiments, three microhabitats (bulk soil, rhizosphere and root endosphere) samples were obtained from two-year-old apple trees grafted on the four different rootstocks. We then investigated the bacterial community composition, diversity, and co-occurrence network in three microhabitats using the Illumina sequencing methods. Only 63 amplicon sequence variants (ASVs) out of a total of 24,485 were shared in the rhizosphere and root endosphere of apple grafted on the four different rootstocks (M9T337, Malus hupehensis Rehd., Malus robusta Rehd., and Malus baccata Borkh.). The core microbiome contained 8 phyla and 25 families. From the bulk soil to the rhizosphere to the root endosphere, the members of the phylum and class levels demonstrated a significant enrichment and depletion pattern. Co-occurrence network analysis showed the network complexity of the rhizosphere was higher than the root endosphere. Most of the keystone nodes in both networks were classified as Proteobacteria, Actinobacteriota and Bacteroidetes and were low abundance species. CONCLUSION: The hierarchical filtration pattern existed not only in the assembly of root endosphere bacteria, but also in the core microbiome. Moreover, most of the core ASVs were high-abundance species, while the keystone ASVs of the network were low-abundance species.

Methanogen-methanotroph community has a more consistent and integrated structure in rice rhizosphere than in bulk soil and rhizoplane.

Methanogenic and methanotrophic microbes together determine the net methane flux from rice fields. Despite much research on them as separate communities, there has been little study of combined community patterns, and how these vary between the rhizoplane (root surface), rhizosphere (soil surrounding the root) and bulk soil around rice plants, especially at larger spatial scale. We collected samples from 32 geographically scattered rice fields in east central China, amplicon targeting the mcrA gene for methanogenesis and pmoA gene for methanotrophy by using high-throughput sequencing. Distinct communities of both methanogens and methanotrophs occurred in each of the three compartments, and predominantly positive links were found between methanogens and methanotrophs in all compartments indicating cross-feeding or consortia relationships. Methanogens were acting as the network hub in the bulk soil, and methanotrophs in rhizoplane. Network complexity and stability was greater in the rhizosphere than rhizoplane and bulk soil, with no network hubs detected, suggesting the strongest effect of homeostatic influence by plant occurred in the rhizosphere. The proportion of determinism (homogeneous selection) and distance-decay relation (DDR) in rhizoplane was consistently lower than that in the rhizosphere for both communities, indicating weaker phylogenetic clustering in rice root surface. Our results have provided a better understanding of CH4 oxidation and emission in rice paddy fields and future agriculture management could take into consideration of the subtle variation among different soil compartments and interactions within methanogenic and methanotrophic communities.

Species pool and local assembly processes drive ß diversity of ammonia-oxidizing and denitrifying microbial communities in rivers along a latitudinal gradient.

Both regional species pool and local community assembly mechanism drive the microbial diversity patterns across geographical gradients. However, little has been done to separate their effects on the ß diversity patterns of microbial communities involved in nitrogen (N) cycling in river ecosystems. Here, we use high-throughput sequencing of the archaeal amoA, bacterial amoA, nirK, and nirS genes, null model, and neutral community model to distinguish the relative importance of species pool and local assembly processes for ammonia-oxidizing and denitrifying communities in river wetlands along a latitudinal gradient in eastern China. Results indicated that the ß diversity of the nirS-type denitrifying community co-varied with γ diversity and environmental heterogeneity, implying that regional species pool and heterogeneous selection explained variation in ß diversity. However, the ß diversity of ammonia-oxidizing and nirK-type denitrifying communities did not correlate with γ diversity and environmental heterogeneity. The continuous hump distribution of ß deviation along the latitudinal gradient and the lower species dispersal rate indicated that the dispersal limitation shaped the variation in ß diversity of ammonia-oxidizing and nirK-type denitrifying communities. Additionally, biotic interactions drove ammonia-oxidizing and nirS-type denitrifying communities by influencing species co-occurrence patterns. Our study highlights the importance of regional species pool and local community assembly processes in shaping geographical patterns of N-cycling microorganisms and extends knowledge of their adaptability to a continuously changing environment on a large scale.

Genetics of anomalies of the kidney and urinary tract with congenital heart disease: A review.

Congenital anomalies of the kidney and urinary tract (CAKUT) and congenital heart disease (CHD) are the most common congenital defects and constitute a major cause of morbidity in children. Anomalies of both systems may be isolated or associated with congenital anomalies of other organ systems. Various reports support the co-occurrence of CAKUT and CHD, although the prevalence can vary. Cardiovascular anomalies occur in 11.2% to 34% of patients with CAKUT, and CAKUT occur in 5.3% to 35.8% of those with CHD. The co-occurrence of genetic factors in both CAKUT and CHD would raise common etiologies including genetics, genetic-environmental interactions, or shared molecular mechanisms and pathways such as NODAL, NOTCH, BMP, WNT, and VEGF. Studies in animal models and humans have indicated a genetic etiology for CHD and CAKUT with hundreds of genes recognized and thousands of entries, found in a catalog of human genetic disorders. There are over 80 CAKUT genes and over 100 CHD genes available for clinical testing. For example, the HNFIB gene accounts for 5% to 31% of reported cases of CAKUT. In view of the association between CAKUT and CHD, a thorough cardiac examination should be performed in patients with CAKUT, and a similar evaluation for CAKUT in the presence of CHD. This will allow early diagnosis and therapeutic intervention to improve the long- term outcome of patients affected, and test for at-risk family members. We present here evidence for an association of anomalies involving the two organ systems, and discuss possible etiologies of targeted genes, their functions, biological processes and interactions on embryogenesis.

Patterns of Grewia (Malvaceae) diversity across geographical scales in Africa and Madagascar.

BACKGROUND AND AIMS: Quantifying spatial species richness is useful to describe biodiversity patterns across broad geographical areas, especially in large, poorly known plant groups. We explore patterns and predictors of species richness across Africa in one such group, the palaeotropical genus Grewia L. (Malvaceae). METHODS: Grewia species richness was quantified by extracting herbarium records from GBIF and Tropicos and creating geographical grids at varying spatial scales. We assessed predictors of species richness using spatial regression models with 30 environmental variables. We explored species co-occurrence in Madagascar at finer resolutions using Schoener's index and compared species range sizes and International Union for Conservation of Nature status among ecoregions. Lastly, we derived a trait matrix for a subset of species found in Madagascar to characterize morphological diversity across space. KEY RESULTS: Grewia species occur in 50 countries in Africa, with the highest number of species in Madagascar (93, with 80 species endemic). Species richness is highest in Madagascar, with ≤23 Grewia species in a grid cell, followed by coastal Tanzania/Kenya (≤13 species) and northern South Africa and central Angola (11 species each). Across Africa, higher species richness was predicted by variables related to aridity. In Madagascar, a greater range in environmental variables best predicted species richness, consistent with geographical grid cells of highest species richness occurring near biome/ecoregion transitions. In Madagascar, we also observe increasing dissimilarity in species composition with increasing geographical distance. CONCLUSIONS: The spatial patterns and underlying environmental predictors that we uncover in Grewia represent an important step in our understanding of plant distribution and diversity patterns across Africa. Madagascar boasts nearly twice the Grewia species richness of the second most species-rich country in Africa, which might be explained by complex topography and environmental conditions across small spatial scales.

Space-time clusters and co-occurrence of Plasmodium vivax and Plasmodium falciparum malaria in West Bengal, India.

BACKGROUND: Malaria, a prominent vector borne disease causing over a million annual cases worldwide, predominantly affects vulnerable populations in the least developed regions. Despite their preventable and treatable nature, malaria remains a global public health concern. In the last decade, India has faced a significant decline in malaria morbidity and mortality. As India pledged to eliminate malaria by 2030, this study examined a decade of surveillance data to uncover space-time clustering and seasonal trends of Plasmodium vivax and Plasmodium falciparum malaria cases in West Bengal. METHODS: Seasonal and trend decomposition using Loess (STL) was applied to detect seasonal trend and anomaly of the time series. Univariate and multivariate space-time cluster analysis of both malaria cases were performed at block level using Kulldorff's space-time scan statistics from April 2011 to March 2021 to detect statistically significant space-time clusters. RESULTS: From the time series decomposition, a clear seasonal pattern is visible for both malaria cases. Statistical analysis indicated considerable high-risk P. vivax clusters, particularly in the northern, central, and lower Gangetic areas. Whereas, P. falciparum was concentrated in the western region with a significant recent transmission towards the lower Gangetic plain. From the multivariate space-time scan statistics, the co-occurrence of both cases were detected with four significant clusters, which signifies the regions experiencing a greater burden of malaria cases. CONCLUSIONS: Seasonal trends from the time series decomposition analysis show a gradual decline for both P. vivax and P. falciparum cases in West Bengal. The space-time scan statistics identified high-risk blocks for P. vivax and P. falciparum malaria and its co-occurrence. Both malaria types exhibit significant spatiotemporal variations over the study area. Identifying emerging high-risk areas of P. falciparum malaria over the Gangetic belt indicates the need for more research for its spatial shifting. Addressing the drivers of malaria transmission in these diverse clusters demands regional cooperation and strategic strategies, crucial steps towards overcoming the final obstacles in malaria eradication.

Metagenomic analysis revealed the potential of lactic acid bacteria in improving natural saline-alkali land.

Management and improving saline-alkali land is necessary for sustainable agricultural development. We conducted a field experiment to investigate the effects of spraying lactic acid bacteria (LAB) on the cucumber and tomato plant soils. Three treatments were designed, including spraying of water, viable or sterilized LAB preparations to the soils of cucumber and tomato plants every 20 days. Spraying sterilized or viable LAB could reduce the soil pH, with a more obvious effect by using viable LAB, particularly after multiple applications. Metagenomic sequencing revealed that the soil microbiota in LAB-treated groups had higher alpha-diversity and more nitrogen-fixing bacteria compared with the water-treated groups. Both viable and sterilized LAB, but not water application, increased the complexity of the soil microbiota interactive network. The LAB-treated subgroups were enriched in some KEGG pathways compared with water or sterilized LAB subgroups, such as environmental information processing-related pathways in cucumber plant; and metabolism-related pathways in tomato plant, respectively. Redundancy analysis revealed association between some soil physico-chemical parameters (namely soil pH and total nitrogen) and bacterial biomarkers (namely Rhodocyclaceae, Pseudomonadaceae, Gemmatimonadaceae, and Nitrosomonadales). Our study demonstrated that LAB is a suitable strategy for decreasing soil pH and improving the microbial communities in saline-alkali land.

Deciphering Microbial Adaptation in the Rhizosphere: Insights into Niche Preference, Functional Profiles, and Cross-Kingdom Co-occurrences.

Rhizosphere microbial communities are to be as critical factors for plant growth and vitality, and their adaptive differentiation strategies have received increasing amounts of attention but are poorly understood. In this study, we obtained bacterial and fungal amplicon sequences from the rhizosphere and bulk soils of various ecosystems to investigate the potential mechanisms of microbial adaptation to the rhizosphere environment. Our focus encompasses three aspects: niche preference, functional profiles, and cross-kingdom co-occurrence patterns. Our findings revealed a correlation between niche similarity and nucleotide distance, suggesting that niche adaptation explains nucleotide variation among some closely related amplicon sequence variants (ASVs). Furthermore, biological macromolecule metabolism and communication among abundant bacteria increase in the rhizosphere conditions, suggesting that bacterial function is trait-mediated in terms of fitness in new habitats. Additionally, our analysis of cross-kingdom networks revealed that fungi act as intermediaries that facilitate connections between bacteria, indicating that microbes can modify their cooperative relationships to adapt. Overall, the evidence for rhizosphere microbial community adaptation, via differences in gene and functional and co-occurrence patterns, elucidates the adaptive benefits of genetic and functional flexibility of the rhizosphere microbiota through niche shifts.

Beyond the Bloom: Unraveling the Diversity, Overlap, and Stability of Free-Living and Particle-Attached Bacterial Communities in a Cyanobacteria-Dominated Hypereutrophic Lake.

In aquatic ecosystems with low nutrient levels, organic aggregates (OAs) act as nutrient hotspots, hosting a diverse range of microbial species compared to those in the water column. Lake eutrophication, marked by intensified and prolonged cyanobacterial blooms, significantly impacts material and energy cycling processes, potentially altering the ecological traits of both free-living (FL) and particle-attached (PA) bacteria. However, the extent to which observed patterns of FL and PA bacterial diversity, community assembly, and stability extend to hypereutrophic lakes remains understudied. To address this gap, we investigated bacterial diversity, composition, assembly processes, and stability within hypereutrophic Lake Xingyun. Our results revealed that FL bacterial communities exhibited higher α-diversity than PA counterparts, coupled with discernible taxonomic compositions. Both bacterial communities showed distinct seasonality, influenced by cyanobacterial bloom intensity. Environmental factors accounted for 71.1% and 54.2% of the variation among FL and PA bacteria, respectively. The assembly of the PA bacterial community was predominantly stochastic, while FL assembly was more deterministic. The FL network demonstrated greater stability, complexity, and negative interactions, indicative of competitive relationships, while the PA network showed a prevalence of positive correlations, suggesting mutualistic interactions. Importantly, these findings differ from observations in oligotrophic, mesotrophic, and eutrophic lakes. Overall, this research provides valuable insights into the interplay among bacterial fractions, enhancing our understanding of nutrient status and cyanobacterial blooms in shaping bacterial communities.

Viral Communities Suppress the Earthworm Gut Antibiotic Resistome by Lysing Bacteria on a National Scale.

Earthworms are critical in regulating soil processes and act as filters for antibiotic resistance genes (ARGs). Yet, the geographic patterns and main drivers of earthworm gut ARGs remain largely unknown. We collected 52 earthworm and soil samples from arable and forest ecosystems along a 3000 km transect across China, analyzing the diversity and abundance of ARGs using shotgun metagenomics. Earthworm guts harbored a lower diversity and abundance of ARGs compared to soil, resulting in a stronger distance-decay rate of ARGs in the gut. Greater deterministic assembly processes of ARGs were found in the gut than in soil. The earthworm gut had a lower frequency of co-occurrence patterns between ARGs and mobile genetic elements (MGEs) in forest than in arable systems. Viral diversity was higher in the gut compared to soil and was negatively correlated with bacterial diversity. Bacteria such as Streptomyces and Pseudomonas were potential hosts of both viruses and ARGs. Viruses had negative effects on the diversity and abundance of ARGs, likely due to the lysis on ARG-bearing bacteria. These findings provide new insights into the variations of ARGs in the earthworm gut and highlight the vital role of viruses in the regulation of ARGs in the soil ecosystem.

Congruence between co-occurrence and trait-based networks is scale-dependent: a case study with flea parasites of small mammalian hosts.

We applied a novel framework based on network theory and a concept of modularity that estimates congruence between trait-based ( = functional) co-occurrence networks, thus allowing the inference of co-occurrence patterns and the determination of the predominant mechanism of community assembly. The aim was to investigate the relationships between species co-occurrence and trait similarity in flea communities at various scales (compound communities: across regions within a biogeographic realm or across sampling sites within a geographic region; component communities: across sampling sites within a geographic region; and infracommunities: within a sampling site). We found that compound communities within biogeographic realms were assembled via environmental or host-associated filtering. In contrast, functional and spatial/host-associated co-occurrence networks, at the scale of regional compound communities, mostly indicated either stochastic processes or the lack of dominance of any deterministic process. Analyses of congruence between functional and either spatial (for component communities) or host-associated (for infracommunities) co-occurrence networks demonstrated that assembly rules in these communities varied among host species. In component communities, stochastic processes prevailed, whereas environmental filtering was indicated in 4 and limiting similarity/competition in 9 of 31 communities. Limiting similarity/competition processes dominated in infracommunities, followed by stochastic mechanisms. We conclude that assembly processes in parasite communities are scale-dependent, with different mechanisms acting at different scales.

Core taxa, co-occurrence pattern, diversity, and metabolic pathways contributing to robust anaerobic biodegradation of chlorophenol.

It is hard to achieve robustness in anaerobic biodegradation of trichlorophenol (TCP). We hypothesized that specific combinations of environmental factors determine phylogenetic diversity and play important roles in the decomposition and stability of TCP-biodegrading bacteria. The anaerobic bioreactor was operated at 35 °C (H condition) or 30 °C (L condition) and mainly fed with TCP (from 28 µM to 180 µM) and organic material. Metagenome sequencing was combined with 16S rRNA gene amplicon sequencing for the microbial community analysis. The results exhibited that the property of robustness occurred in specific conditions. The corresponding co-occurrence and diversity patterns suggest high collectivization, degree and evenness for robust communities. Two types of core functional taxa were recognized: dechlorinators (unclassified Anaerolineae, Thermanaerothrix and Desulfovibrio) and ring-opening members (unclassified Proteobacteria, Methanosarcina, Methanoperedens, and Rubrobacter). The deterministic process of the expansion of niche of syntrophic bacteria at higher temperatures was confirmed. The reductive and hydrolytic dechlorination mechanisms jointly lead to C-Cl bond cleavage. H ultimately adapted to the stress of high TCP loading, with more abundant ring-opening enzyme (EC 3.1.1.45, ∼55%) and hydrolytic dechlorinase (EC 3.8.1.5, 26.5%) genes than L (∼47%, 10.5%). The functional structure (based on KEGG) in H was highly stable despite the high loading of TCP (up to 60 µM), but not in L. Furthermore, an unknown taxon with multiple functions (dechlorinating and ring-opening) was found based on genetic sequencing; its functional contribution of EC 3.8.1.5 in H (26.5%) was higher than that in L (10.5%), and it possessed a new metabolic pathway for biodegradation of halogenated aromatic compounds. This new finding is supplementary to the robust mechanisms underlying organic chlorine biodegradation, which can be used to support the engineering, regulation, and design of synthetic microbiomes.

Impacts of oxbow lake evolution on sediment microbial community structure in the Yellow River source region.

Oxbow lake formation and evolution have significant impacts on the fragile Yellow River Basin ecosystem. However, the effects of different oxbow lake evolutionary stages on sediment microbial community structure are not yet understood comprehensively. Therefore, microbial community structure in three stages of oxbow lake succession, namely, lotic lake (early stage), semi-lotic lake (middle stage), and lentic lake (late stage), was investigated in the present study in the Yellow River Basin on the Qinghai-Tibet Plateau. Amplicon sequencing was employed to reveal differences in microbial community diversity and composition. The bacterial and fungal communities in sediment were significantly different among the three succession stages and were driven by different environmental factors. In particular, bacterial community structure was influenced primarily by nitrate-nitrogen (N), microbial biomass phosphorus, and total carbon (C) and organic C in the early, middle, and late stages, respectively. Conversely, fungal community structure was influenced primarily by ammonium-N in the early stage and by moisture content in the middle and late stages. However, the predicted functions of the microbial communities did not exhibit significant differences across the three succession stages. Both bacteria and fungi were influenced significantly by stochastic factors. Homogeneous selection had a high relative contribution to bacteria community assembly in the middle stage, whereas the relative contributions of heterogeneous selection processes to fungal community assembly increased through the three stages. As succession time increased, the total number of keystone species increased gradually, and the late succession stage had high network complexity and the highest network stability. The findings could facilitate further elucidation of the evolution mechanisms of oxbow lake source area, high-altitude river evolution dynamics, in addition to aiding a deeper understanding of the long-term ecological evolution patterns of source river ecosystems.