Distinct correlation network of clinical characteristics in suicide attempters having adolescent major depressive disorder with non-suicidal self-injury.

Suicidal behavior and non-suicidal self-injury (NSSI) are common in adolescent patients with major depressive disorder (MDD). Thus, delineating the unique characteristics of suicide attempters having adolescent MDD with NSSI is important for suicide prediction in the clinical setting. Here, we performed psychological and biochemical assessments of 130 youths having MDD with NSSI. Participants were divided into two groups according to the presence/absence of suicide attempts (SAs). Our results demonstrated that the age of suicide attempters is lower than that of non-attempters in participants having adolescent MDD with NSSI; suicide attempters had higher Barratt Impulsiveness Scale (BIS-11) impulsivity scores and lower serum CRP and cortisol levels than those having MDD with NSSI alone, suggesting levels of cortisol and CRP were inversely correlated with SAs in patients with adolescent MDD with NSSI. Furthermore, multivariate regression analysis revealed that NSSI frequency in the last month and CRP levels were suicidal ideation predictors in adolescent MDD with NSSI, which may indicate that the increased frequency of NSSI behavior is a potential risk factor for suicide. Additionally, we explored the correlation between psychological and blood biochemical indicators to distinguish suicide attempters among participants having adolescent MDD with NSSI and identified a unique correlation network that could serve as a marker for suicide attempters. Our research data further suggested a complex correlation between the psychological and behavioral indicators of impulsivity and anger. Therefore, our study findings may provide clues to identify good clinical warning signs for SA in patients with adolescent MDD with NSSI.

Data-driven prediction of colonization outcomes for complex microbial communities.

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Data-driven prediction of colonization outcomes for complex microbial communities.

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

Silica Nanoparticles Promote Apoptosis in Ovarian Granulosa Cells via Autophagy Dysfunction.

Although silica nanoparticles (SNPs) are generally thought to be biocompatible and safe, the adverse effects of SNPs were also reported in previous studies. SNPs cause follicular atresia via the induction of ovarian granulosa cell apoptosis. However, the mechanisms for this phenomenon are not well understood. This study focuses on exploring the relationship between autophagy and apoptosis induced by SNPs in ovarian granulosa cells. Our results showed that 25.0 mg/kg body weight (b.w.)/intratracheal instillation of 110 nm in diameter spherical Stöber SNPs caused ovarian granulosa cell apoptosis in follicles in vivo. We also found that SNPs mainly internalized into the lumens of the lysosomes in primary cultured ovarian granulosa cells in vitro. SNPs induced cytotoxicity via a decrease in viability and an increase in apoptosis in a dose-dependent manner. SNPs increased BECLIN-1 and LC3-II levels, leading to the activation of autophagy and increased P62 level, resulting in the blockage of autophagic flux. SNPs increased the BAX/BCL-2 ratio and cleaved the caspase-3 level, resulting in the activation of the mitochondrial-mediated caspase-dependent apoptotic signaling pathway. SNPs enlarged the LysoTracker Red-positive compartments, decreased the CTSD level, and increased the acidity of lysosomes, leading to lysosomal impairment. Our results reveal that SNPs cause autophagy dysfunction via lysosomal impairment, resulting in follicular atresia via the enhancement of apoptosis in ovarian granulosa cells.

Spatial profiling of microbial communities by sequential FISH with error-robust encoding.

Spatial analysis of microbiomes at single cell resolution with high multiplexity and accuracy has remained challenging. Here we present spatial profiling of a microbiome using sequential error-robust fluorescence in situ hybridization (SEER-FISH), a highly multiplexed and accurate imaging method that allows mapping of microbial communities at micron-scale. We show that multiplexity of RNA profiling in microbiomes can be increased significantly by sequential rounds of probe hybridization and dissociation. Combined with error-correction strategies, we demonstrate that SEER-FISH enables accurate taxonomic identification in complex microbial communities. Using microbial communities composed of diverse bacterial taxa isolated from plant rhizospheres, we apply SEER-FISH to quantify the abundance of each taxon and map microbial biogeography on roots. At micron-scale, we identify clustering of microbial cells from multiple species on the rhizoplane. Under treatment of plant metabolites, we find spatial re-organization of microbial colonization along the root and alterations in spatial association among microbial taxa. Taken together, SEER-FISH provides a useful method for profiling the spatial ecology of complex microbial communities in situ.

Isolation and Characterization of Phosphorus Solubilizing Bacteria With Multiple Phosphorus Sources Utilizing Capability and Their Potential for Lead Immobilization in Soil.

Phosphorus solubilizing bacteria (PSB) can promote the level of plant-absorbable phosphorus (P) in agro-ecosystems. However, little attention has been paid to PSB harboring abilities in utilizing multiple phosphorus sources and their potentials for heavy metal immobilization. In this study, we applied the strategy of stepwise acclimation by using Ca3(PO4)2, phytate, FePO4, and AlPO4 as sole P source. We gained 18 PSB possessing abilities of multiple P sources utilization, and these bacteria belonged to eight genera (Acinetobacter, Pseudomonas, Massilia, Bacillus, Arthrobacter, Stenotrophomonas, Ochrobactrum, and Cupriavidus), and clustered to two apparent parts: Gram-positive bacteria and Gram-negative bacteria. The isolate of Acinetobacter pittii gp-1 presented good performance for utilizing Ca3(PO4)2, FePO4, AlPO4, and phytate, with corresponding P solubilizing levels were 250.77, 46.10, 81.99, and 7.91 mg/L PO4 3--P, respectively. The PSB A. pittii gp-1 exhibited good performance for solubilizing tricalcium phosphate in soil incubation experiments, with the highest values of water soluble P and available P were 0.80 and 1.64 mg/L, respectively. Additionally, the addition of A. pittii gp-1 could promote the immobilization of lead (Pb), and the highest Pb immobilization efficiency reached 23%. Simultaneously, we found the increases in abundances of both alkaline phosphatase gene (phoD) and ß-propeller phytase gene (bpp) in strain gp-1 added soils. Besides, we observed the expression up-regulation of both pyrroloquinoline quinone gene (pqq) and polyphosphate kinases gene (ppk), with the highest relative expression levels of 18.18 and 5.23, respectively. We also found the polyphosphate particles using granule staining. To our knowledge, our findings first suggest that the solubilizing of tricalcium phosphate by phosphorus solubilizing bacterium belonging to Acinetobacter is coupled with the synthesis of polyphosphate. Taken together, A. pittii gp-1 could be a good candidate in improving soil fertility and quality.

Dynamic motility selection drives population segregation in a bacterial swarm.

Population expansion in space, or range expansion, is widespread in nature and in clinical settings. Space competition among heterogeneous subpopulations during range expansion is essential to population ecology, and it may involve the interplay of multiple factors, primarily growth and motility of individuals. Structured microbial communities provide model systems to study space competition during range expansion. Here we use bacterial swarms to investigate how single-cell motility contributes to space competition among heterogeneous bacterial populations during range expansion. Our results revealed that motility heterogeneity can promote the spatial segregation of subpopulations via a dynamic motility selection process. The dynamic motility selection is enabled by speed-dependent persistence time bias of single-cell motion, which presumably arises from physical interaction between cells in a densely packed swarm. We further showed that the dynamic motility selection may contribute to collective drug tolerance of swarming colonies by segregating subpopulations with transient drug tolerance to the colony edge. Our results illustrate that motility heterogeneity, or "motility fitness," can play a greater role than growth rate fitness in determining the short-term spatial structure of expanding populations.

Responses of the rhizosphere bacterial community in acidic crop soil to pH: Changes in diversity, composition, interaction, and function.

Soil pH is an important predictor of bacterial community composition and diversity. Examining the effects of pH on diversity, structure, interaction, and function of rhizosphere bacterial communities in acidic crop soils provide valuable information for knowing potential role of rhizosphere bacteria in crop yield. Here, we collected soils from artificial greenhouses and applied Illumina Miseq sequencing, quantitative PCR techniques, multiple ecological analysis methods, including topological analysis and functional profiling to analyze our data and validate our hypotheses. We found that the soil physicochemical properties, species diversity, and rhizosphere bacterial community composition were significantly affected by the degree of soil acidification (pH < 5.5 and pH > 5.5) but not vegetation type. Additionally, bacterial absolute abundance increased with higher pH. The 18 soil samples were clustered into two distinct groups of pH < 5.5 and pH > 5.5 at the OTU level, and soil pH had more of an effect on bacterial community composition compared to the other physicochemical variables. In addition, rhizosphere bacteria might presented relatively less competition for survival in pH < 5.5 soils, and bacterial community functions, including nutrient (i.e., carbon, nitrogen, phosphorus, and sulphur) cycling-related enzymes and proteins, were downregulated in more acidic soils (pH < 5.5) based on sequence analysis. To our knowledge, this report is the first to show that pH is a key factor affecting the diversity, structure, interaction, and function of rhizosphere bacterial communities in acidic crop soil in artificial greenhouses. Our findings emphasize that community function and structure of rhizosphere bacteria are closely correlated in more acidic soils, and the decreased crop yield may be correlated with attenuation of the function of the rhizosphere bacterial community.

Alkaline phosphatase-harboring bacterial community and multiple enzyme activity contribute to phosphorus transformation during vegetable waste and chicken manure composting.

The objective of this study was to evaluate changes in phosphorus fractions during vegetable waste and chicken manure composting. High throughput sequencing, quantitative PCR, and multiple analysis methods were applied to investigate interconnections among phosphorus fractions, enzyme activity, and phoD-harboring bacterial community composition. We found the highest composting temperature reached 61 °C and phosphorus fractions presented significant differences during a 60-day composting. The content of plant-absorbable phosphorus, including water soluble phosphorus, available phosphorus, and citric acid phosphorus increased by 121%, 87%, and 63%, respectively. Additionally, phoD gene abundance significantly correlated with the activities of nine enzymes. Our findings emphasize that microbial activity plays an important role in phosphorus transformation during composting, and the final composting product could be good biological phosphorus fertilizer. To our knowledge, this is the first report indicating that enzyme activity, community composition and abundance of phoD-harboring bacteria have direct and indirect effects on phosphorus transformation during composting.

Resonant frequency and damping of a coupled resonant system measured by apparent dissipation factor frequency spectra.

This paper proposes a novel scheme to study the resonance of a vibration mode of a complex system by investigating the apparent dissipation factor frequency spectrum (ADFFS) of another coupled vibration mode. We demonstrate that the resonant absorption peak in ADFFS of one vibration mode is proportional to the displacement amplitude square of another coupled vibration mode from both energy dissipation and dynamics. Two coupled micro-cantilevers are experimentally shown that the ADFFS of one micro-cantilever could be applied to obtain the resonant frequency and the damping of the other coupled micro-cantilever.