PTK6: An emerging biomarker for prognosis and immunotherapeutic response in clear cell renal carcinoma (KIRC).

Kidney renal clear cell carcinoma (KIRC), one of the most prevalent form of kidney carcinoma, is highly aggressive cancer known for significant immune infiltration and high mortality rates. The absence of sensitivity to traditional therapy has spurred the search for new treatments. Protein Tyrosine Kinase 6 (PTK6) is implicated in promoting cancer growth, spread, and metastasis. Our review of The Cancer Genome Atlas database revealed PTK6 overexpression in KIRC, though its specific role in this cancer type was unclear. We investigated PTK6's cancer-promoting roles in KIRC using the database and confirmed our findings with patient-derived tissues. Our analysis showed that elevated PTK6 expression is linked to worse outcomes and higher levels of immune infiltration. It also correlates positively with neo-antigens (NEO) and DNA ploidy changes in KIRC. This research delves into PTK6's role in KIRC development, suggesting PTK6 as a possible biomarker for prognosis and treatment in KIRC.

Multi-dimensional characterization of apoptosis in the tumor microenvironment and therapeutic relevance in melanoma.

PURPOSE: Melanoma is widely utilized as a prominent model for the development of immunotherapy, thought an inadequate immune response can occur. Moreover, the development of apoptosis-related therapies and combinations with other therapeutic strategies is impeded by the limited understanding of apoptosis's role within diverse tumor immune microenvironments (TMEs). METHODS: Here, we constructed an apoptosis-related tumor microenvironment signature (ATM) and employ multi-dimensional analysis to understand the roles of apoptosis in tumor microenvironment. We further assessed the clinical applications of ATM in nine independent cohorts, and anticipated the impact of ATM on cellular drug response in cultured cells. RESULTS: Our ATM model exhibits robust performance in survival prediction in multiple melanoma cohorts. Different ATM groups exhibited distinct molecular signatures and biological processes. The low ATM group exhibited significant enrichment in B cell activation-related pathways. What's more, plasma cells showed the lowest ATM score, highlighting their role as pivotal contributors in the ATM model. Mechanistically, the analysis of the interplay between plasma cells and other immune cells elucidated their crucial role in orchestrating an effective anti-tumor immune response. Significantly, the ATM signature exhibited associations with therapeutic efficacy of immune checkpoint blockade and the drug sensitivity of various agents, including FDA-approved and clinically utilized drugs targeting the VEGF signaling pathway. Finally, ATM was associated with tertiary lymphoid structures (TLS), exhibiting stronger patient stratification ability compared to classical "hot tumors". CONCLUSION: Our findings indicate that ATM is a prognostic factor and is associated with the immune response and drug sensitivity in melanoma.

Multiomics characterization of pyroptosis in the tumor microenvironment and therapeutic relevance in metastatic melanoma.

BACKGROUND: Pyroptosis, mediated by gasdermins with the release of multiple inflammatory cytokines, has emerged as playing an important role in targeted therapy and immunotherapy due to its effectiveness at inhibiting tumor growth. Melanoma is one of the most commonly used models for immunotherapy development, though an inadequate immune response can occur. Moreover, the development of pyroptosis-related therapy and combinations with other therapeutic strategies is limited due to insufficient understanding of the role of pyroptosis in the context of different tumor immune microenvironments (TMEs). METHODS: Here, we present a computational model (pyroptosis-related gene score, PScore) to assess the pyroptosis status. We applied PScore to 1388 melanoma samples in our in-house cohort and eight other publicly available independent cohorts and then calculated its prognostic power of and potential as a predictive marker of immunotherapy efficacy. Furthermore, we performed association analysis for PScore and the characteristics of the TME by using bulk, single-cell, and spatial transcriptomics and assessed the association of PScore with mutation status, which contributes to targeted therapy. RESULTS: Pyroptosis-related genes (PRGs) showed distinct expression patterns and prognostic predictive ability in melanoma. Most PRGs were associated with better survival in metastatic melanoma. Our PScore model based on genes associated with prognosis exhibits robust performance in survival prediction in multiple metastatic melanoma cohorts. We also found PScore to be associated with BRAF mutation and correlate positively with multiple molecular signatures, such as KRAS signaling and the IFN gamma response pathway. Based on our data, melanoma with an immune-enriched TME had a higher PScore than melanoma with an immune-depleted or fibrotic TME. Additionally, monocytes had the highest PScore and malignant cells and fibroblasts the lowest PScore based on single-cell and spatial transcriptome analyses. Finally, a higher PScore was associated with better therapeutic efficacy of immune checkpoint blockade, suggesting the potential of pyroptosis to serve as a marker of immunotherapy response. CONCLUSIONS: Collectively, our findings indicate that pyroptosis is a prognostic factor and is associated with the immune response in metastatic melanoma, as based on multiomics data. Our results provide a theoretical basis for drug combination and reveal potential immunotherapy response markers.

The impacts of biologic treatment on metabolic profiling in psoriasis.

Psoriasis is an immune-mediated inflammatory disease commonly accompanied by various metabolic disorders. It is widely known that biologics could affect the metabolic status and comorbidities in psoriasis patients, however, the effects of biologics on metabolism in psoriasis patients remain poorly understood. The aim of this study was to elucidate the characteristic changes of metabolic profiling in psoriasis vulgaris (PsV) patients before and after applying biologics. Plasma samples were collected from a retrospective cohort of 43 PsV patients. Non-targeted metabolomics analyses were performed using liquid chromatography-mass spectrometry (LC-MS) to compare the metabolic profiles before and after applying adalimumab (ADA) or ixekizumab (IXE) for 4 weeks. Additionally, correlation analyses were conducted to investigate the associations between metabolite expression levels and clinical characteristics. The biologics significantly affected the metabolic profiles of PsV patients especially in glycerophospholipids (GPs). First, phosphatidylcholine (PC), unsaturated lysophosphatidylcholine (LPC), unsaturated lysophosphatidic acid (LPA) and unsaturated lysophosphatidylethanolamine (LPE) were significantly up-regulated, whereas phosphatidylethanolamine (PE), saturated LPC, saturated LPA and saturated LPE were predominantly down-regulated after biologic treatment. What is more, the changes in PE and LPA were mainly observed after applying IXE instead of ADA. Second, we also found GPs including PC, unsaturated LPC, unsaturated LPA and unsaturated LPE were primarily negatively correlated with disease severity, whereas, PE, saturated LPC, saturated LPA and saturated LPE displayed inverse correlations. Biologics could affect GP metabolism and facilitate the transition of metabolic status from a pro-inflammatory to an anti-inflammatory phenotype in PsV patients.

Exploring the association and causal effect between white blood cells and psoriasis using large-scale population data.

Introduction: Psoriasis is a chronic inflammatory disease of the skin. A few studies have shown that psoriasis is an immune-mediated disease in which multiple immune cells play crucial roles. However, the association between circulating immune cells and psoriasis remains elusive. Methods: To explore the role of circulating immune cells in psoriasis, 361,322 individuals from the UK Biobank (UKB) and 3,971 patients with psoriasis from China were included to investigate the association between white blood cells and psoriasis via an observational study. Genome-wide association studies (GWAS) and Mendelian randomization (MR) were used to evaluate the causal relationship between circulating leukocytes and psoriasis. Results: The risk of psoriasis increased with high levels of monocytes, neutrophils, and eosinophils (relative risks and 95% confidence intervals, respectively: 1.430 (1.291-1.584) for monocytes, 1.527 (1.379-1.692) for neutrophils, and 1.417 (1.294-1.551) for eosinophils). Upon further MR analysis, eosinophils showed a definite causal relationship with psoriasis (odds ratio of inverse-variance weighted: 1.386, 95% confidence intervals: 1.092-1.759) and a positive correlation with the psoriasis area and severity index (PASI) score (P = 6.6 × 10-5). The roles of the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR) in psoriasis were also assessed. More than 20,000 genetic variations associated with NLR, PLR, and LMR were discovered in a GWAS analysis using the UKB data. Following adjustment for covariates in the observational study, NLR and PLR were shown to be risk factors for psoriasis, whereas LMR was a protective factor. MR results indicated that there was no causal relationship between these three indicators and psoriasis; however, NLR, PLR, and LMR correlated with the PASI score (NLR: rho = 0.244, P = 2.1 × 10-21; PLR: rho = 0.113, P = 1.4 × 10-5; LMR: rho = -0.242, P = 3.5×10-21). Discussion: Our findings revealed an important association between circulating leukocytes and psoriasis, which is instructive for the clinical practice of psoriasis treatment.

Single-cell profiling of ineffective erythropoiesis in a mouse model of ß-thalassaemia intermedia.

Beta-thalassaemia is an inherited haemoglobin disorder characterised by ineffective erythropoiesis (IE). The detailed pathogenesis of IE remains unclear. In this study, we used single-cell RNA sequencing (scRNA-seq) to examine IE in Th3/+ ß-thalassaemic mice. The results showed that the erythroid group was remarkably expanded, and genes involved in biological processes such as iron metabolism, haeme synthesis, protein folding, and response to heat were significantly upregulated from erythroid progenitors to reticulocytes in ß-thalassaemic mice. In particular, we identified a unique cell population close to reticulocytes, named ThReticulocytes, characterised by a high level of heat shock protein 70 (Hsp70) expression and dysregulation of iron metabolism and haeme synthesis signalling. Treatment of ß-thalassaemic mice with the haeme oxygenase inhibitor tin-mesoporphyrin effectively improved the iron disorder and IE, and the ThReticulocyte population and Hsp70 expression were significantly suppressed. This study revealed in detail the progression of IE at the single-cell level and possibly provided clues to find therapeutic targets in thalassaemia.

Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma.

Background: Melanoma is among the most malignant immunologic tumor types and is associated with high mortality. However, a considerable number of melanoma patients cannot benefit from immunotherapy owing to individual differences. This study attempts to build a novel prediction model of melanoma that fully considers individual differences in the tumor microenvironment. Methods: An immune-related risk score (IRRS) was constructed based on cutaneous melanoma data from The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was used to calculate immune enrichment scores of 28 immune cell signatures. We performed pairwise comparisons to obtain scores for cell pairs based on the difference in the abundance of immune cells within each sample. The resulting cell pair scores, in the form of a matrix of relative values of immune cells, formed the core of the IRRS. Results: The area under the curve (AUC) for the IRRS was over 0.700, and when the IRRS was combined with clinical information, the AUC reached 0.785, 0.817, and 0.801 for the 1-, 3-, and 5-year survival, respectively. Differentially expressed genes between the two groups were enriched in staphylococcal infection and estrogen metabolism pathway. The low IRRS group showed a better immunotherapeutic response and exhibited more neoantigens, richer T-cell receptor and B-cell receptor diversity, and higher tumor mutation burden. Conclusion: The IRRS enables a good prediction of prognosis and immunotherapy effect, based on the difference in the relative abundance of different types of infiltrating immune cells, and could provide support for further research in melanoma.

Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis.

Psoriatic arthritis (PsA) is associated with psoriasis, featured by its irreversible joint symptoms. Despite the significant impact on the healthcare system, it is still challenging to leverage machine learning or statistical models to predict PsA and its progression, or analyze drug efficacy. With 3961 patients' clinical records, we developed a machine learning model for PsA diagnosis and analysis of PsA progression risk, respectively. Furthermore, general additive models (GAMs) and the Kaplan-Meier (KM) method were applied to analyze the efficacy of various drugs on psoriasis treatment and inhibiting PsA progression. The independent experiment on the PsA prediction model demonstrates outstanding prediction performance with an AUC score of 0.87 and an AUPR score of 0.89, and the Jackknife validation test on the PsA progression prediction model also suggests the superior performance with an AUC score of 0.80 and an AUPR score of 0.83, respectively. We also identified that interleukin-17 inhibitors were the more effective drug for severe psoriasis compared to other drugs, and methotrexate had a lower effect in inhibiting PsA progression. The results demonstrate that machine learning and statistical approaches enable accurate early prediction of PsA and its progression, and analysis of drug efficacy.

Multi-omics characterization of autophagy-related molecular features for therapeutic targeting of autophagy.

Autophagy is a major contributor to anti-cancer therapy resistance. Many efforts have been made to understand and overcome autophagy-mediated therapy resistance, but these efforts have been unsuccessful in clinical applications. In this study, we establish an autophagy signature to estimate tumor autophagy status. We then classify approximately 10,000 tumor samples across 33 cancer types from The Cancer Genome Atlas into autophagy score-high and autophagy score-low groups. We characterize the associations between multi-dimensional molecular features and tumor autophagy, and further analyse the effects of autophagy status on drug response. In contrast to the conventional view that the induction of autophagy serves as a key resistance mechanism during cancer therapy, our analysis reveals that autophagy induction may also sensitize cancer cells to anti-cancer drugs. We further experimentally validate this phenomenon for several anti-cancer drugs in vitro and in vivo, and reveal that autophagy inducers potentially sensitizes tumor cells to etoposide through downregulating the expression level of DDIT4. Our study provides a comprehensive landscape of molecular alterations associated with tumor autophagy and highlights an opportunity to leverage multi-omics analysis to utilize multiple drug sensitivity induced by autophagy.

Sphingosine kinase 1 promotes tumor immune evasion by regulating the MTA3-PD-L1 axis.

Immune checkpoint blockade (ICB) exhibits considerable benefits in malignancies, but its overall response rate is limited. Previous studies have shown that sphingosine kinases (SPHKs) are critical in the tumor microenvironment (TME), but their role in immunotherapy is unclear. We performed integrative analyses including bioinformatics analysis, functional study, and clinical validation to investigate the role of SPHK1 in tumor immunity. Functionally, we demonstrated that the inhibition of SPHK1 significantly suppressed tumor growth by promoting antitumor immunity in immunocompetent melanoma mouse models and tumor T-cell cocultures. A mechanistic analysis revealed that MTA3 functions as the downstream target of SPHK1 in transcriptionally regulating tumor PD-L1. Preclinically, we found that anti-PD-1 monoclonal antibody (mAb) treatment significantly rescued tumor SPHK1 overexpression or tumor MTA3 overexpression-mediated immune evasion. Significantly, we identified SPHK1 and MTA3 as biological markers for predicting the efficacy of anti-PD-1 mAb therapy in melanoma patients. Our findings revealed a novel role for SPHK1 in tumor evasion mediated by regulating the MTA3-PD-L1 axis, identified SPHK1 and MTA3 as predictors for assessing the efficacy of PD-1 mAb treatment, and provided a therapeutic possibility for the treatment of melanoma patients.

Characterization of Immune-Related Alternative Polyadenylation Events in Cancer Immunotherapy.

Alternative polyadenylation (APA) is an important posttranscriptional modification commonly involved in tumor development. However, the functional roles of APA in tumor immunity remain largely unknown. Here, we performed an in-depth analysis of the 3'UTR usage of protein-coding genes and tumor immune response in 10,303 tumor samples across 31 cancer types to develop the immune-related APA event (ImmAPA) score pipeline, an integrated algorithm to characterize the regulatory landscape of APA events in cancer immunity-related pathways. Tumor-specific ImmAPAs that strongly correlate with immune cell infiltration and immune checkpoint blockade (ICB) treatment-related biomarkers were identified. Among these ImmAPAs, the top-ranking COL1A1 3'UTR usage was strongly associated with worse prognosis and tumor immune evasion. Furthermore, a machine learning approach to construct an ICB-related ImmAPA score model predicted immunotherapy efficacy. Overall, the characterization of immune-related APA that corresponds to tumor progression and tumor immunity highlights the clinical utility of APA events as potential biomarkers in cancer immunotherapy. SIGNIFICANCE: Elucidation of the landscape of immune-related alternative polyadenylation in cancer identifies alternative polyadenylation events that may play a role in immune modulation and immunotherapy efficacy.

Albendazole induces immunotherapy response by facilitating ubiquitin-mediated PD-L1 degradation.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have been increasingly used in patients with various cancers and have shown efficient therapeutic outcomes. However, fewer than 40% of cases across multiple cancer types show a response to ICIs. Therefore, developing more efficient combinational approaches with ICIs and revealing the underlying mechanisms are important goals for achieving rapid clinical transformation and application. METHODS: The effects on antitumor immunity activity of albendazole (ABZ) and the synergistic effects of ABZ with CD73 blockade were investigated in the melanoma B16F10 and the Lewis lung cancer tumor-bearing immune-competent mice models. The mechanism of ABZ reducing PD-L1 protein level through suppressing UBQLN4 was identified and validated through immunoprecipitation-mass spectrometry and molecular methods. Bioinformatics and anti-PD-1 therapy melanoma patients samples analysis were used to assess the level of UBQLN4/PD-L1 in the therapeutic efficacy of anti-PD-1 therapy. RESULTS: ABZ induces CD8+ T cell activity and subsequent immunotherapy response associated with suppression of PD-L1 protein level. Mechanistically, we revealed that ABZ promotes ubiquitin-mediated degradation of PD-L1 via suppressing UBQLN4, which was bound to PD-L1 and stabilized PD-L1 protein. Preclinically, genetic deletion or target inhibition of CD73 showed synergistic effects with ABZ treatment in the immune-competent mice models. Significantly, UBQLN4 and PD-L1 levels were higher in the tumor region of responders versus non-responders and correlated with better progression-free survival and overall survival in anti-PD-1 therapy melanoma patients. CONCLUSIONS: Our findings revealed a previously unappreciated role of ABZ in antitumor immunity by inducing ubiquitin-mediated PD-L1 protein degradation, identified predictors for assessing the therapeutic efficacy of anti-PD-1 therapy, and provided novel therapeutic possibility by combination treatment of ABZ and CD73 blockade in cancers.

Associations of combined lifestyle and genetic risks with incident psoriasis: A prospective cohort study among UK Biobank participants of European ancestry.

BACKGROUND: Whether the lifestyle is associated with the risk of psoriasis in the presence of different genetic risk levels remains unknown. OBJECTIVE: To examine the gene-behavior interaction in association with incident psoriasis. METHODS: This study is based on the data from the UK Biobank, which recruited 500,000 participants. Genetic risk was categorized into low, intermediate, and high groups. The lifestyle score comprised the body mass index, smoking, physical activity, and diet and was also categorized into the ideal, intermediate, and poor groups. Within each genetic risk group, the risks of incident psoriasis associated with each lifestyle level were investigated and compared with the low genetic risk and ideal lifestyle group. RESULTS: Compared with the low genetic risk and ideal lifestyle group, the poor lifestyle and high genetic risk group was associated with a hazard ratio of up to 4.625 (95% confidence interval [CI], 2.920-7.348) for psoriasis. There was no interaction between genetic risk and lifestyle. The population attributable fractions of lifestyle and genetic risk were 32.2% (95% CI, 25.1%-38.6%) and 13.0% (95% CI, 3.2%-21.8%), respectively. LIMITATIONS: No verification in other independently ascertained populations. CONCLUSION: Lifestyle factors are predictive of the risk of incident psoriasis independent of genetic risk, and the relative impact of lifestyle factors was greater than that of genetic risk.

Identification of immune checkpoint and cytokine signatures associated with the response to immune checkpoint blockade in gastrointestinal cancers.

Immune checkpoint blockade (ICB) of the programmed cell death 1/programmed cell death ligand 1 (PD-1/PD-L1) immune checkpoint pathway has led to unprecedented advances in cancer therapy. However, the overall response rate of anti-PD-1/PD-L1 monotherapy is still unpromising, underscoring the need for predictive biomarkers. In this retrospective study, we collected pretreatment plasma samples from two independent cohorts of patients receiving ICB. To determine whether a signature of plasma cytokines could be associated with therapeutic efficacy, we systemically profiled cytokine clusters and functional groups in the discovery and validation datasets by using 59 multiplexed bead immunoassays and bioinformatics analysis. We first attempted to functionally classify the 59 immunological factors according to their biological classification or functional roles in the cancer-immunity cycle. Surprisingly, we observed that two signatures, the "checkpoint signature" and "trafficking of T-cell signature", were higher in the response subgroup than in the nonresponse subgroup in both the discovery and validation cohorts. Moreover, enrichment of the "checkpoint signature" was correlated with improved overall survival and progression-free survival in both datasets. In addition, we demonstrated that increased baseline levels of three checkpoint molecules (PD-L1, T-cell immunoglobulin mucin receptor 3 and T-cell-specific surface glycoprotein CD28) were common peripheral responsive correlates in both cohorts, thus rendering this "refined checkpoint signature" an ideal candidate for future verification. In the peripheral blood system, the "refined checkpoint signature" may function as a potential biomarker for anti-PD-1/PD-L1 monotherapy in gastrointestinal (GI) cancers.

A Machine Learning Approach Yields a Multiparameter Prognostic Marker in Liver Cancer.

A number of staging systems have been developed to predict clinical outcomes in hepatocellular carcinoma (HCC). However, no general consensus has been reached regarding the optimal model. New approaches such as machine learning (ML) strategies are powerful tools for incorporating risk factors from multiple platforms. We retrospectively reviewed the baseline information, including clinicopathologic characteristics, laboratory parameters, and peripheral immune features reflecting T-cell function, from three HCC cohorts. A gradient-boosting survival (GBS) classifier was trained with prognosis-related variables in the training dataset and validated in two independent cohorts. We constructed a 20-feature GBS model classifier incorporating one clinical feature, 14 laboratory parameters, and five T-cell function parameters obtained from peripheral blood mononuclear cells. The GBS model-derived risk scores demonstrated high concordance indexes (C-indexes): 0.844, 0.827, and 0.806 in the training set and validation sets 1 and 2, respectively. The GBS classifier could separate patients into high-, medium- and low-risk subgroups with respect to death in all datasets (P < 0.05 for all comparisons). A higher risk score was positively correlated with a higher clinical stage and the presence of portal vein tumor thrombus (PVTT). Subgroup analyses with respect to Child-Pugh class, Barcelona Clinic Liver Cancer stage, and PVTT status supported the prognostic relevance of the GBS-derived risk algorithm independent of the conventional tumor staging system. In summary, a multiparameter ML algorithm incorporating clinical characteristics, laboratory parameters, and peripheral immune signatures offers a different approach to identify patients with the greatest risk of HCC-related death.

Peripheral cytokine levels as predictive biomarkers of benefit from immune checkpoint inhibitors in cancer therapy.

Currently, only a small subset of cancer patients can benefit from anti-PD-1/PD-L1 monotherapy, indicating that further predictive biomarkers are needed. In the retrospective study, plasma samples were collected before anti-PD-L1/PD-L1 treatment in two subsets of patients. A total of 59 immunological factors, including cytokines, chemokines, and soluble immune checkpoints, were measured by using a multiplex immunoassay kit. Moreover, multiplex immunohistochemistry (mIHC) was performed in a subgroup of patients. In the discovery cohort, multiplex immunoassay profiling data revealed that both soluble PD-L1 and C-C motif chemokine 5 (CCL5/RANTES) showed rising trends across the three subgroups PD, SD and CR/PR. Further investigation demonstrated the predictive and prognostic value of the pre-treatment levels of PD-L1, CCL5/RANTES, and their combinatorial signature the "2-cytokine signature". As expected, the signature-high patients displayed a remarkably increased disease control rate (DCR) and prolonged survival versus that of the lower subgroup. More importantly, the relevance between the three signatures and the efficiency of immunotherapy was confirmed in the pan-cancer validation cohort. Notably, the significant association between the "2-cytokine signature" and longer survival was validated. Further quantitative analyses of the tumor microenvironment composition suggested a link between the "2-cytokine signature" and NK cell infiltration. In conclusion, a combined peripheral signature comprising CCL5/RANTES and soluble PD-L1 appears to be an effective biomarker to predict benefit from anti-PD-1/PD-L1 monotherapy. Our study underscores that peripheral immunological features may play an essential role in guiding patient selection and are worthy of future prospective investigations.

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Riparian zones, the critical ecological interfaces between terrestrial and aquatic ecosystems, are species rich habitats. However, riparian zones are seriously threatened by human activities in the world. Riparian invertebrates represent a large proportion of riparian biodiversity, perform various ecological functions, and provide an essential link between aquatic and terrestrial ecosystems. Although many studies have investigated the riparian invertebrate communities, there is lacking a comprehensive summary of maintaining mechanisms underlying riparian invertebrate diversity. This review discussed seven characteristics of riparian zones that might support high riparian invertebrate diversity: flood and drought, nutrient, microhabitat diversity, riparian vegetation, microclimate gradients, food resources and river spatial gradients. Further, we summarized the maintaining mechanisms of riparian invertebrate diversity. Disturbances of periodic flood and drought trigger the reproduction and migration of invertebrates, increase the turnover of invertebrate communities, and create suitable conditions for riparian invertebrates. Adequate nutrients support a high invertebrate diversity by increasing primary productivity of riparian habitats. Elevated microhabitat diversity provides a variety of niche space for specialist riparian invertebrates. Strong microclimate gradients provide complex and diverse habitats and thus facilitate the coexistence of aquatic and terrestrial invertebrates in riparian zones. Cross-ecosystem resource subsidies increase food availability and contribute unique food sources to riparian invertebrates. The differentiation of these factors along river longitudinal and lateral gradients provides conditions for the diversification of riparian invertebrates at a larger scale. Understanding the maintaining mechanisms of riparian invertebrate diversity is important for conservation of riparian biodiversity and integrated management of river ecosystems.

An ecological scenario prediction model for newly created wetlands caused by coal mine subsidence in the Yanzhou, China.

The ecological model we developed can simulate the state of wetlands and predict ecosystem development by varying both parameter settings and forcing functions. The newly created wetland resulting from large-scale coal mining is a distinct type of wetland, but existing ecological models for this wetland type are limited in number and scope. The Yanzhou coalfield, located in Shandong Province in China, contains a typical newly created wetland that came into being after coal mining subsidence. We developed an ecological model for this wetland that estimates values for four state variables: phytoplankton biomass (A), zooplankton biomass (Z), sediment biomass (D), and hydrophyte biomass (H). Analysis of the results showed that the model was sensitive to changes in nutrient loading. As nutrient loads increased, plankton biomass increased, and the ratio of zooplankton biomass to phytoplankton biomass (Z/A) decreased. We defined three prediction scenarios for the wetland and calculated their eco-exergies to compare the ecological effects for each scenario. The most effective measures to improve the state of the ecosystem are to reduce the subsidence depth and to decrease nutrient loading.

Diversity and Distribution of Riparian Arthropods in the Drawdown Zone of China's Three Gorges Reservoir.

Riparian zones are interesting habitats as they are important transitional zones between terrestrial and aquatic ecosystems, but highly threatened by human disturbances. They support a high arthropod diversity as they experience periodic flooding disturbance and sharp environmental gradients. Their associated arthropod fauna are of high conservation value. Nevertheless, their arthropod diversity remains largely unknown, and its distribution pattern along elevational gradients is poorly understood. Few data are available on the effects of flood regimes and other factors in determining riparian arthropod communities. In this study, we investigated the diversity and distribution of riparian arthropods along an elevational gradient and determined the major factors structuring the arthropod communities in the drawdown zone of the Three Gorges Reservoir, China. Significant compositional and structural changes of riparian arthropod communities were observed along the test elevational gradient. The abundance and richness of riparian arthropods increased with elevation. The relative abundance of predators decreased with elevation, whereas the saprovores and omnivores showed an upward trend along the elevational gradient. Redundancy analysis showed that there were significant interactions between the flood regimes, plant communities, and soil conditions. Among these environmental factors studied, flood duration was the main factor in structuring the riparian arthropod communities. Conservation and restoration strategies should consider flood duration in the operation of large reservoirs because riparian arthropods are particularly sensitive to flood regimes.