Mycorrhizal effects on crop yield and soil ecosystem functions in a long-term tillage and fertilization experiment

It is well understood that agricultural management influences arbuscular mycorrhizal (AM) fungi, but there is controversy about whether farmers should manage for AM symbiosis. We assessed AM fungal communities colonizing wheat roots for three consecutive years in a long-term (> 14 yr) tillage and fertilization experiment. Relationships among mycorrhizas, crop performance, and soil ecosystem functions were quantified. Tillage, fertilizers and continuous monoculture all reduced AM fungal richness and shifted community composition toward dominance of a few ruderal taxa. Rhizophagus and Dominikia were depressed by tillage and/or fertilization, and their abundances as well as AM fungal richness correlated positively with soil aggregate stability and nutrient cycling functions across all or no-tilled samples. In the field, wheat yield was unrelated to AM fungal abundance and correlated negatively with AM fungal richness. In a complementary glasshouse study, wheat biomass was enhanced by soil inoculum from unfertilized, no-till plots while neutral to depressed growth was observed in wheat inoculated with soils from fertilized and conventionally tilled plots. This study demonstrates contrasting impacts of low-input and conventional agricultural practices on AM symbiosis and highlights the importance of considering both crop yield and soil ecosystem functions when managing mycorrhizas for more sustainable agroecosystems.

RNA threading with secondary structure and sequence profile

MOTIVATION

RNA threading aims to identify remote homologies for template-based modeling of RNA 3D structure. Existing RNA alignment methods primarily rely on secondary structure alignment. They are often time- and memory-consuming, limiting large-scale applications. In addition, the accuracy is far from satisfactory.

RESULTS

Using RNA secondary structure and sequence profile, we developed a novel RNA threading algorithm, named RNAthreader. To enhance the alignment process and minimize memory usage, a novel approach has been introduced to simplify RNA secondary structures into compact diagrams. RNAthreader employs a two-step methodology. Initially, integer programming and dynamic programming are combined to create an initial alignment for the simplified diagram. Subsequently, the final alignment is obtained using dynamic programming, taking into account the initial alignment derived from the previous step. The benchmark test on 80 RNAs illustrates that RNAthreader generates more accurate alignments than other methods, especially for RNAs with pseudoknots. Another benchmark, involving 30 RNAs from the RNA-Puzzles experiments, exhibits that the models constructed using RNAthreader templates have a lower average RMSD than those created by alternative methods. Remarkably, RNAthreader takes less than two hours to complete alignments with ∼5000 RNAs, which is 3-40 times faster than other methods. These compelling results suggest that RNAthreader is a promising algorithm for RNA template detection.

AVAILABILITY AND IMPLEMENTATION

https://yanglab.qd.sdu.edu.cn/RNAthreader.

Diagnostic performance of SPECT in lumbar spondylolysis: a systematic review and meta-analysis

AIM

To evaluate the diagnostic value and clinical applicability of single-photon-emission computed tomography (SPECT) for lumbar spondylolysis using meta-analysis.

MATERIALS AND METHODS

Stata 12.0, was used to test the heterogeneity, and the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and other effect sizes were collected to generate the summary receiver operating characteristic (SROC) curve for comprehensive evaluation. Meta-regression analysis was used to explore the source of heterogeneity, and subgroup analysis was performed. Funnel plots, Fagan's line diagrams, and likelihood ratio dot plots were drawn to evaluate publication bias and clinical applicability.

RESULTS

Eight studies involving 785 patients were included. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, odds ratio, and area under the SROC curve of SPECT for the diagnosis of lumbar spondylolysis were 0.85 (95% confidence interval [CI]: 0.70 0.93), 0.92 (95% CI: 0.60 0.99), 11.01 (95% CI: 1.61 75.18), 0.17 (95% CI: 0.08 0.35), 0.92 (95% CI: 0.90 0.94). Meta-regression analysis showed that the sources of heterogeneity were region and age. Subgroup analysis showed that the specificity of the child and adolescent subgroup was significantly higher than that of the middle-aged and elderly subgroups. Deek's funnel plots showed no significant publication bias. The pooled effect of the likelihood ratio dot plot for diagnosis is in the upper-right quadrant.

CONCLUSION

As a diagnostic tool for spondylolysis, SPECT has a high degree of specificity, moderate sensitivity, and relatively high diagnostic effectiveness. It can be used as an auxiliary sign in the diagnosis and treatment of lumbar spondylolysis.

Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15

We present the monomer and multimer structure prediction results of our methods in CASP15. We first designed an elaborate pipeline that leverages complementary sequence databases and advanced database searching algorithms to generate high-quality multiple sequence alignments (MSAs). Top MSAs were then selected for the subsequent step of structure prediction. We utilized trRosettaX2 and AlphaFold2 for monomer structure prediction (group name Yang-Server), and AlphaFold-Multimer for multimer structure prediction (group name Yang-Multimer). Yang-Server and Yang-Multimer are ranked at the top and the fourth, respectively, for monomer and multimer structure prediction. For 94 monomers, the average TM-score of the predicted structure models by Yang-Server is 0.876, compared to 0.798 by the default AlphaFold2 (i.e., the group NBIS-AF2-standard). For 42 multimers, the average DockQ score of the predicted structure models by Yang-Multimer is 0.464, compared to 0.389 by the default AlphaFold-Multimer (i.e., the group NBIS-AF2-multimer). Detailed analysis of the results shows that several factors contribute to the improvement, including improved MSAs, iterated modeling for large targets, interplay between monomer and multimer structure prediction for intertwined structures, etc. However, the structure predictions for orphan proteins and multimers remain challenging, and breakthroughs in this area are anticipated in the future.

trRosettaRNA: automated prediction of RNA 3D structure with transformer network

RNA 3D structure prediction is a long-standing challenge. Inspired by the recent breakthrough in protein structure prediction, we developed trRosettaRNA, an automated deep learning-based approach to RNA 3D structure prediction. The trRosettaRNA pipeline comprises two major steps: 1D and 2D geometries prediction by a transformer network; and 3D structure folding by energy minimization. Benchmark tests suggest that trRosettaRNA outperforms traditional automated methods. In the blind tests of the 15th Critical Assessment of Structure Prediction (CASP15) and the RNA-Puzzles experiments, the automated trRosettaRNA predictions for the natural RNAs are competitive with the top human predictions. trRosettaRNA also outperforms other deep learning-based methods in CASP15 when measured by the Z-score of the Root-Mean-Square Deviation. Nevertheless, it remains challenging to predict accurate structures for synthetic RNAs with an automated approach. We hope this work could be a good start toward solving the hard problem of RNA structure prediction with deep learning.

Maize, wheat, and soybean root traits depend upon soil phosphorus fertility and mycorrhizal status

Strong effects of plant identity, soil nutrient availability or mycorrhizal fungi on root traits have been well documented, but their interactive influences on root traits are still poorly understood. Here, three crop species (maize, wheat and soybean) were grown under four phosphorus (P) addition levels (0, 20, 40 and 60 mg P kg-1 dry soil), and plants were inoculated with or without five combined arbuscular mycorrhizal fungal (AMF) species. Plant biomass, nutrient contents, root traits (including total root length, average root diameter, specific root length and root tissue density) and plants' mycorrhizal responses were measured. Crop species, P level, AMF, and their interactions strongly affected plant biomass and root traits. P fertilization promoted plant growth but reduced mycorrhizal benefits on plant biomass and nutrient uptake. Root traits of maize were sensitive to P addition only under the non-mycorrhizal condition, whilst most root traits of soybean and wheat plants were responsive to mycorrhizal inoculation but not P addition. Mycorrhizal colonization reduced the root plasticity in response to P fertility for maize but not for wheat or soybean. This study highlights the importance of soil nutrient fertility and mycorrhizal symbiosis in influencing root traits.

[Analysis of suspected occupational diseases cases and subsequent diagnosis follow-up investigation in Hubei Province from 2020 to 2021]

Objective: To understand the epidemiological characteristics of suspected occupational diseases cases, and to track the subsequent diagnosis of suspected cases in Hubei Province from 2020 to 2021, and to provide theoretical basis for the supervision of suspected occupational diseases. Methods: In April 2022, the data of suspected occupational diseases cases and occupational diseases in Hubei Province from 2020 to 2021 were collected by the Occupational Diseases and Health Risk Factors Information Surveillance System. The distribution and diagnosis of suspected occupational diseases cases were analyzed. We investigated undiagnosed suspected occupational diseases by telephone. Results: From 2020 to 2021, a total of 1872 cases of suspected occupational diseases in 6 categories and 18 species were reported in Hubei Province. The top three suspected occupational diseases were suspected occupational noise deafness (36.75%, 688/1872), suspected coal worker's pneumoconiosis (33.07%, 619/1872) and suspected silicosis (20.99%, 393/1872). The diagnosis rate of suspected occupational diseases was 33.60% (629/1872). The rate of confirmed diagnosis was 63.59% (400/629). The diagnosis rate (26.86%, 456/1698) and rate of confirmed diagnosis (55.48%, 253/456) of suspected occupational diseases detected by occupational health examination were the lowest. The diagnosis rate of suspected occupational diseases detected by comprehensive medical institutions and private medical institutions were lower than disease prevention and control institutions and occupational disease prevention center (P<0.05). The main reasons for not entering the diagnostic procedure included that workers were not informed that they were diagnosed as suspected occupational diseases (31.55%, 124/393), workers were unwilling to apply for occupational disease diagnosis (18.56%, 73/393), and some workers planned to apply for diagnosis but had not yet applied (10.69%, 42/393) . Conclusion: Occupational noise deafness, coal worker's pneumoconiosis and silicosis are the main diseases of suspected occupational diseases in Hubei Province. In order to increase the diagnosis rate and confirmed diagnosis rate of suspected occupational diseases, it is suggested to strengthen management and supervision from the aspects of case management, information warning and worker notification.

A cryogenic ion trap for single molecule vibrational spectroscopy

We report on an ion trapping system for performing a novel form of cryogenic messenger spectroscopy with single molecule sensitivity. The system features a cryogenic radio-frequency ion trap loaded with single analyte molecules via a quadrupole mass filter. We demonstrate the ability to controllably attach inert gas particles to buffer gas cooled, trapped molecular ions. Sympathetic cooling by co-trapped, laser cooled 88Sr+ further reduces the translational temperature of trapped molecules to the millikelvin regime. We verify the presence of cryogenic "tags" via non-destructive optical mass spectrometry and selectively remove these adducts by resonantly driving vibrational transitions in the tagged molecular ions. This enables us to derive the vibrational spectrum of a single analyte molecule from the frequency dependence of the tag detachment rate. We have demonstrated these capabilities by measuring transitions in the C-H stretching region for single cationic fragments of both indole (C8H7N) and 1,3-benzodioxole (C6H4O2CH2). These capabilities are not reliant on a specific molecular level structure and thus constitute a general, non-destructive method for vibrational spectroscopy of individual molecular ions.

A unified approach to protein domain parsing with inter-residue distance matrix

MOTIVATION

It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing.

RESULTS

Based on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis.

AVAILABILITY AND IMPLEMENTATION

https://yanglab.nankai.edu.cn/UniDoc/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information

One of key features of intrinsically disordered regions (IDRs) is facilitation of protein-protein and protein-nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/.

Microsomal glutathione S-transferase 1 targets the autophagy signaling pathway to suppress ferroptosis in gastric carcinoma cells

OBJECTIVE

Ferroptosis is a newly discovered form of programmed cell death; however, the specific mechanisms that regulate ferroptosis have yet to be fully elucidated in gastric carcinoma. In this study, we aimed to investigate how microsomal glutathione S-transferase 1 (MGST1) regulates ferroptosis in gastric carcinoma cells.

METHODS

Gastric adenocarcinoma (SGC7901) cells that overexpressed MGST1 or expressed only low levels of MGST1, were treated with specific compounds (erastin, sorafenib, RSL3, MK-2206 and SC79). Then, we detected the levels of malondialdehyde (MDA), glutathione (GSH), iron and reactive oxygen species (ROS). Protein expression levels of the non-classical autophagy and protein kinase B (Akt)/glycogen synthase kinase-3β (GSK-3β) pathways were determined by western blotting and cell viability was analyzed by Cell Counting Kit-8 (CCK-8) assays. The expressions of target genes were detected using qRT-PCR.

RESULTS

We evaluated a range of ferroptosis-inducing compounds and found that MGST1 expression was down-regulated during ferroptosis in SGC7901 cells. The ferroptosis inducer RSL3 played a role in classical ferroptotic events while the overexpression of MGST1 impaired these effects. Interestingly, the overexpression of MGST1 resulted in the inactivation of autophagy by repressing the expression of ATG16L1 and the conversion of LC3-I to LC3-II. The upregulation of ATG16L1 eliminated the inhibitory action of MGST1 on ferroptosis. Notably, the overexpression of MGST1 induced the activation of the Akt/GSK-3β pathway. An Akt inhibitor antagonized the inhibitory effects of MGST1 on autophagy and ferroptosis.

CONCLUSION

Collectively, our findings demonstrate a novel molecular mechanism and signaling pathway for ferroptosis. We also characterized that the overexpression of MGST1 induces gastric carcinoma cell proliferation by activating the Akt/GSK-3β signaling pathway.

Single-sequence protein structure prediction using supervised transformer protein language models

Significant progress has been made in protein structure prediction in recent years. However, it remains challenging for AlphaFold2 and other deep learning-based methods to predict protein structure with single-sequence input. Here we introduce trRosettaX-Single, an automated algorithm for single-sequence protein structure prediction. It incorporates the sequence embedding from a supervised transformer protein language model into a multi-scale network enhanced by knowledge distillation to predict inter-residue two-dimensional geometry, which is then used to reconstruct three-dimensional structures via energy minimization. Benchmark tests show that trRosettaX-Single outperforms AlphaFold2 and RoseTTAFold on orphan proteins and works well on human-designed proteins (with an average template modeling score (TM-score) of 0.79). An experimental test shows that the full trRosettaX-Single pipeline is two times faster than AlphaFold2, using much fewer computing resources (<10%). On 2,000 designed proteins from network hallucination, trRosettaX-Single generates structure models with high confidence. As a demonstration, trRosettaX-Single is applied to missense mutation analysis. These data suggest that trRosettaX-Single may find potential applications in protein design and related studies.

[Effect of peer education on knowledge, attitude and practices towards schistosomiasis among primary school students in endemic foci of Wuhan City]

OBJECTIVE

To evaluate the effect of peer education on knowledge, attitude and practices towards schistosomiasis control among primary school students in endemic foci of Wuhan City.

METHODS

Yucai Hankou Primary School in Jiang' an District, Wuhan City was selected in 2021, and all students at grades 4 and 5 were sampled using a cluster sampling method to receive peer education about schistosomiasis. The changes of knowledge, attitude and practices towards schistosomiasis control were compared before and after peer education to evaluate the effect of peer education on knowledge, attitude and practices towards schistosomiasis control.

RESULTS

The overall awareness of schistosomiasis control knowledge and the overall proportion of correct attitudes towards schistosomiasis control and correct schistosomiasis-related behaviors increased from 51.50%, 93.70% and 92.99% before peer education to 86.50%, 98.98% and 98.72% after peer education among primary school students, respectively (χ²=149.457, 21.692 and 20.691, all P values < 0.05). The overall awareness of schistosomiasis control knowledge and the overall proportion of correct attitudes towards schistosomiasis control and correct schistosomiasis-related behaviors were 49.19%, 92.20% and 92.72% among Grade 4 primary school students and 53.83%, 95.21% and 93.28% among Grade 5 primary school students prior to peer education (χ² = 1.214, 2.034 and 0.096, all P values > 0.05), and increased to 75.93%, 98.09% and 97.59% among Grade 4 primary school students and 97.16%, 99.87% and 99.87% among Grade 5 primary school students after peer education, respectively (χ² = 40.798, 9.572, 7.207, 133.194, 9.678 and 14.926, all P values < 0.05). The overall awareness of schistosomiasis control knowledge and the overall proportion of correct attitudes towards schistosomiasis control and correct schistosomiasis-related behaviors were 51.25%, 76.92% and 77.97% among male primary school students and 51.80%, 94.42% and 95.70% among female primary school students prior to peer education, the differences in the overall proportion of correct attitudes towards schistosomiasis control and correct schistosomiasis-related behaviors between male primary school students and female primary school students were significant (χ² = 30.462 and 33.416, both P values < 0.05), and increased to 86.23%, 98.25% and 97.79% among male primary school students and 86.83%, 99.85% and 99.85% among female primary school students after peer education (χ² = 0.081, 3.529 and 3.335, all P values > 0.05), respectively.

CONCLUSIONS

Peer education is effective to improve the knowledge, attitude and practices towards schistosomiasis control among primary school students in endemic foci of Wuhan City, which may be more effective to improve the awareness of schistosomiasis control knowledge and proportion of correct schistosomiasis-related behaviors among primary school students at high grades.

[Assessment of MS-Score and HScore in timeliness of diagnosis of macrophage activation syndrome associated with adult-onset Still's disease]

The data of 33 patients with adult-onset still's disease (AOSD)-associated macrophage activation syndrome (MAS) were retrospectively collected from January 2013 to December 2020 in Peking Union Medical College Hospital. Hemophagocytic lymphohistiocytosis (HLH)-2004 criteria, macrophage activation syndrome/juvenile idiopathic arthritis (MS-Score) and hemophagocytic syndrome diagnostic score (HScore) were used to diagnose AOSD-associated MAS, respectively. The time of diagnosis of AOSD-associated MAS by MS-Score was 19.0 (4.5, 31.0) days [M (Q₁,Q₃)] earlier than by HLH-2004 criteria, and 13.5 (0.5, 21.5) days earlier than by HScore (both P<0.05). The difference was not statistically significant between the time of diagnosis of AOSD-associated MAS by Hscore and by HLH-2004 criteria (P>0.05). There was significant difference among the three criteria (P<0.001). MS-Score can be used to diagnose AOSD-associated MAS earlier than HLH-2004 criteria, while the timeliness of HScore is not certain.

[Analysis of hepatic pathological inflammation and fibrosis condition and its influencing factors in 721 patients with chronic hepatitis B with normal ALT]

Objective: To analyze the hepatic pathological inflammation and fibrosis condition in order to explore the relationship with related clinical indicators in patients with chronic hepatitis B patients with normal alanine aminotransferase (ALT). Methods: 721 cases of chronic hepatitis B with normal ALT who were initially diagnosed in the Department of Infectious Diseases of Henan Provincial People's Hospital from August 2016 to December 2019 were retrospectively collected. Liver biopsy was performed in all patients. General data of patients such as gender, age, liver function indexes, blood routine indexes, HBsAg level, HBeAg status, HBV DNA level, spleen thickness and prothrombin time were collected. Univariate and multivariate analysis methods were used to determine the influencing factors of inflammation and fibrosis degree with liver biopsy. A receiver operating characteristic curve (ROC) was used to evaluate the established multi-factor prediction model. Alpha=0.05 was considered as a standard orientation of test. Results: The average age of 721 cases with chronic hepatitis B was 36.1±9.7 years, and the male to female ratio was 1.28/1, with inflammation and fibrosis grade mainly concentrated in G1S1 (349 cases), G1S2 (132 cases), G2S2 (119 cases), and G2S1 (57 cases). Among them, there were 349 (48.4%) cases of G1S1, and 372 (51.6%) cases of G/S≥2. The main manifestations were mild to moderate inflammation and fibrosis, and only 64 (8.88%) cases had severe G/S≥3. HBsAg level (stratified with 4 log10 IU/ml as the boundary) analyzed in 721 cases were correlated with the relevant clinical indicators stratification and liver pathological inflammation and fibrosis, and the difference was statistically significant (inflammation grade, χ²=6.182, P=0.013; Fibrosis grade, χ²=36.534, P=0.001). Univariate analysis of the relevant clinical indicators that may influence the patient's liver pathological G/S ≥2 showed the patient's age, albumin, γ- glutamyltransferase (GGT), platelet, prothrombin time (PT), spleen thickness and HBsAg level were all statistically significant (P<0.05), while multivariate analysis showed that age, GGT, PT, and spleen thickness had statistical differences (P<0.05). The prediction model was established in accordance to multivariate analysis, and the area under the ROC curve was 0.642. Maximization of the sum of sensitivity and specificity as cut-off value of Logit P=0.497, the diagnostic sensitivity, specificity, and Youden's index were 60.6%, 64.5%, and 0.252, respectively. Conclusion: More than half of patients with chronic hepatitis B with normal ALT have significant inflammation and fibrosis and require timely antiviral therapy. Age, GGT, PT and spleen thickness can help comprehensively evaluate the liver inflammation and fibrosis status among patients, but the lack of accurate prediction models suggests that more effective indicators that can help predict the inflammation and fibrosis status of such patients have yet to be discovered. Therefore, liver biopsy should still be actively performed in patients with normal ALT to confirm the diagnosis and timely treatment.

[Effects of sodium iodide symporter co-expression on proliferation and cytotoxic activity of chimeric antigen receptor T cells in vitro]

OBJECTIVE

To investigate the effects of co-expression of sodium iodide symporter (NIS) reporter gene on the proliferation and cytotoxic activity of chimeric antigen receptor (CAR)-T cells in vitro.

METHODS

T cells expressing CD19 CAR (CAR-T cells), NIS reporter gene (NIS-T cells), and both (NIS-CAR-T cells) were prepared by lentiviral infection. The transfection rates of NIS and CAR were determined by flow cytometry, and the cell proliferation rate was assessed using CCK-8 assay at 24, 48 and 72 h of routine cell culture. The T cells were co-cultured with Nalm6 tumor cells at the effector-target ratios of 1∶2, 1∶1, 2∶1 and 4∶1 for 24, 48 and 72 h, and the cytotoxicity of CAR-T cells to the tumor cells was evaluated using lactate dehydrogenase (LDH) assay. ELISA was used to detect the release of IFN-γ and TNF-β in the co-culture supernatant, and the function of NIS was detected with iodine uptake test.

RESULTS

The CAR transfection rate was 91.91% in CAR-T cells and 99.41% in NIS-CAR-T cells; the NIS transfection rate was 47.83% in NIS-T cells and 50.24% in NIS- CAR-T cells. No significant difference in the proliferation rate was observed between CAR-T and NIS-CAR-T cells cultured for 24, 48 or 72 h (P> 0.05). In the co-cultures with different effector-target ratios, the tumor cell killing rate was significantly higher in CAR-T group than in NIS-CAR-T group at 24 h (P < 0.05), but no significant difference was observed between the two groups at 48 h or 72 h (P>0.05). Higher IFN-γ and TNF-β release levels were detected in both CAR-T and NIS-CAR-T groups than in the control group (P < 0.05). NIS-T cells and NIS-CAR-T cells showed similar capacity of specific iodine uptake (P>0.05), which was significantly higher than that in the control T cells (P < 0.05).

CONCLUSION

The co-expression of the NIS reporter gene does not affect CAR expression, proliferation or tumor cell-killing ability of CAR-T cells.

Grazing exclusion had greater effects than nitrogen addition on soil and plant community in a desert steppe, Northwest of China

BACKGROUND

The impacts of increasing nitrogen (N) deposition and overgrazing on terrestrial ecosystems have been continuously hot issues. Grazing exclusion, aimed at restoration of grassland ecosystem function and service, has been extensively applied, and considered a rapid and effective vegetation restoration method. However, the synthetic effects of exclosure and N deposition on plant and community characteristics have rarely been studied. Here, a 4-year field experiment of N addition and exclusion treatment had been conducted in the desert steppe dominated by Alhagi sparsifolia and Lycium ruthenicum in northwest of China, and the responses of soil characteristics, plant nutrition and plant community to the treatments had been analyzed.

RESULTS

The grazing exclusion significantly increased total N concentration in the surface soil (0-20 cm), and increased plant height, coverage (P < 0.05) and aboveground biomass. Specifically, A. sparsifolia recovered faster both in individual and community levels than L. ruthenicum did after exclusion. There was no difference in response to N addition gradients between the two plants.

CONCLUSIONS

Our findings suggest that it is exclusion rather than N addition that has greater impacts on soil properties and plant community in desert steppe. Present N deposition level has no effect on plant community of desert steppe based on short-term experimental treatments.

Toward the assessment of predicted inter-residue distance

MOTIVATION

Significant progress has been achieved in distance-based protein folding, due to improved prediction of inter-residue distance by deep learning. Many efforts are thus made to improve distance prediction in recent years. However, it remains unknown what is the best way of objectively assessing the accuracy of predicted distance.

RESULTS

A total of 19 metrics were proposed to measure the accuracy of predicted distance. These metrics were discussed and compared quantitatively on three benchmark datasets, with distance and structure models predicted by the trRosetta pipeline. The experiments show that a few metrics, such as distance precision, have a high correlation with the model accuracy measure TM-score (Pearson's correlation coefficient >0.7). In addition, the metrics are applied to rank the distance prediction groups in CASP14. The ranking by our metrics coincides largely with the official version. These data suggest that the proposed metrics are effective for measuring distance prediction. We anticipate that this study paves the way for objectively monitoring the progress of inter-residue distance prediction. A web server and a standalone package are provided to implement the proposed metrics.

AVAILABILITY AND IMPLEMENTATION

http://yanglab.nankai.edu.cn/APD.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Practical Indicators for Risk of Airborne Transmission in Shared Indoor Environments and Their Application to COVID-19 Outbreaks

Some infectious diseases, including COVID-19, can undergo airborne transmission. This may happen at close proximity, but as time indoors increases, infections can occur in shared room air despite distancing. We propose two indicators of infection risk for this situation, that is, relative risk parameter (H_r) and risk parameter (H). They combine the key factors that control airborne disease transmission indoors: virus-containing aerosol generation rate, breathing flow rate, masking and its quality, ventilation and aerosol-removal rates, number of occupants, and duration of exposure. COVID-19 outbreaks show a clear trend that is consistent with airborne infection and enable recommendations to minimize transmission risk. Transmission in typical prepandemic indoor spaces is highly sensitive to mitigation efforts. Previous outbreaks of measles, influenza, and tuberculosis were also assessed. Measles outbreaks occur at much lower risk parameter values than COVID-19, while tuberculosis outbreaks are observed at higher risk parameter values. Because both diseases are accepted as airborne, the fact that COVID-19 is less contagious than measles does not rule out airborne transmission. It is important that future outbreak reports include information on masking, ventilation and aerosol-removal rates, number of occupants, and duration of exposure, to investigate airborne transmission.

Practical Indicators for Risk of Airborne Transmission in Shared Indoor Environments and Their Application to COVID-19 Outbreaks

Some infectious diseases, including COVID-19, can undergo airborne transmission. This may happen at close proximity, but as time indoors increases, infections can occur in shared room air despite distancing. We propose two indicators of infection risk for this situation, that is, relative risk parameter (H_r) and risk parameter (H). They combine the key factors that control airborne disease transmission indoors: virus-containing aerosol generation rate, breathing flow rate, masking and its quality, ventilation and aerosol-removal rates, number of occupants, and duration of exposure. COVID-19 outbreaks show a clear trend that is consistent with airborne infection and enable recommendations to minimize transmission risk. Transmission in typical prepandemic indoor spaces is highly sensitive to mitigation efforts. Previous outbreaks of measles, influenza, and tuberculosis were also assessed. Measles outbreaks occur at much lower risk parameter values than COVID-19, while tuberculosis outbreaks are observed at higher risk parameter values. Because both diseases are accepted as airborne, the fact that COVID-19 is less contagious than measles does not rule out airborne transmission. It is important that future outbreak reports include information on masking, ventilation and aerosol-removal rates, number of occupants, and duration of exposure, to investigate airborne transmission.

On Monomeric and Multimeric Structures-Based Protein-Ligand Interactions

Many ligands simultaneously interact with multiple protein chains in quaternary structure (QS). However, a significant number of previous studies on template-based modeling of protein-ligand interactions were based on monomeric structure (MS), which may suffer from incomplete binding information. The defects of using MS rather than QS have not been systematically studied before. In this work, based on molecular docking experiments and binding free energy estimations, we performed a large-scale comparison of the protein-ligand interactions in both forms of structures. We found that 1) about 18.6 percent biologically relevant ligands bind multiple chains in QS simultaneously. 2) For more than 95 percent complexes with multiple chains involved in the interactions, the binding free energy is lower for the QS form than the MS form. 3) For over 70 percent complexes with multi-chain binding pockets, docking with QS yields more accurate ligand conformations than with MS. While for about 1.82 percent complexes, accurate docking conformations were obtained by MS. Based on this work, it is encouraged to make use of QS rather than MS in future studies on protein-ligand interactions.

Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates

The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure prediction is presented. The major improvement over trRosetta consists of two folds. The first is the application of a new multi-scale network, i.e., Res2Net, for improved prediction of inter-residue geometries, including distance and orientations. The second is an attention-based module to exploit multiple homologous templates to increase the accuracy further. Compared with trRosetta, trRosettaX improves the contact precision by 6% and 8% on the free modeling targets of CASP13 and CASP14, respectively. A preliminary version of trRosettaX is ranked as one of the top server groups in CASP14's blind test. Additional benchmark test on 161 targets from CAMEO (between Jun and Sep 2020) shows that trRosettaX achieves an average TM-score ≈0.8, outperforming the top groups in CAMEO. These data suggest the effectiveness of using the multi-scale network and the benefit of incorporating homologous templates into the network. The trRosettaX algorithm is incorporated into the trRosetta server since Nov 2020. The web server, the training and inference codes are available at: https://yanglab.nankai.edu.cn/trRosetta/.

The trRosetta server for fast and accurate protein structure prediction

The trRosetta (transform-restrained Rosetta) server is a web-based platform for fast and accurate protein structure prediction, powered by deep learning and Rosetta. With the input of a protein's amino acid sequence, a deep neural network is first used to predict the inter-residue geometries, including distance and orientations. The predicted geometries are then transformed as restraints to guide the structure prediction on the basis of direct energy minimization, which is implemented under the framework of Rosetta. The trRosetta server distinguishes itself from other similar structure prediction servers in terms of rapid and accurate de novo structure prediction. As an illustration, trRosetta was applied to two Pfam families with unknown structures, for which the predicted de novo models were estimated to have high accuracy. Nevertheless, to take advantage of homology modeling, homologous templates are used as additional inputs to the network automatically. In general, it takes ~1 h to predict the final structure for a typical protein with ~300 amino acids, using a maximum of 10 CPU cores in parallel in our cluster system. To enable large-scale structure modeling, a downloadable package of trRosetta with open-source codes is available as well. A detailed guidance for using the package is also available in this protocol. The server and the package are available at https://yanglab.nankai.edu.cn/trRosetta/ and https://yanglab.nankai.edu.cn/trRosetta/download/ , respectively.

Intra-couple discordance in preconception syphilis screening for both spouses: a national and population-based survey in China, 2013-2018

OBJECTIVE

The antenatal screening strategy remains inadequate for eliminating congenital syphilis. To further eliminate maternal fetal transmission, preconception syphilis screening is considered an option. In this study, we investigated syphilis seropositivity and intra-couple discordance among married couples planning a pregnancy in China to provide essential baseline evidence for preconception syphilis screening.

DESIGN

Population-based survey.

SETTING

National preconception registered data.

POPULATION

Married Chinese couples planning conception within 6 months between 2013 and 2018.

METHODS

Syphilis was screened using rapid plasma reagin (RPR); infection self-reporting and sociodemographic characteristics were collected through questionnaires and medical records, respectively. r 3.2.2 and arcgis 10.2 were used for statistical analyses and geographic mapping.

MAIN OUTCOME MEASURES

RPR seropositivity.

RESULTS

Among 31 955 041 couples, 29 737 172 (93.06%) had complete RPR results for both spouses; of those, 0.62% (186 100) were seropositive, with dramatic intra-couple discordance, with 0.33% positivity in wives, 0.24% positivity in husbands and 0.05% positivity in both spouses. Across time, both seropositivity and intra-couple discordance remained stable. Seropositivity in different regions varied significantly, with provincial rates ranging geographically from Tibet (0.8%) to Hebei (0.2%) (P < 0.05). Economic level was an independent factor for this regional variation, with seropositivity increasing as gross domestic product income decreased (P < 0.05).

CONCLUSIONS

Intra-couple discordance in seropositivity for syphilis is notable among couples, with a considerable rate of pre-existing syphilis before pregnancy. Thus, screening both spouses during integrated preconception health care is recommended for further eliminating maternal-fetal transmission.

TWEETABLE ABSTRACT

Intra-couple discordance in seropositivity for syphilis is notable among couples, with a considerable rate of pre-existing syphilis before pregnancy. Screening both spouses during integrated preconception health care is recommended to further eliminate maternal-fetal transmission.

Improved estimation of model quality using predicted inter-residue distance

MOTIVATION

Protein model quality assessment (QA) is an essential component in protein structure prediction, which aims to estimate the quality of a structure model and/or select the most accurate model out from a pool of structure models, without knowing the native structure. QA remains a challenging task in protein structure prediction.

RESULTS

Based on the inter-residue distance predicted by the recent deep learning-based structure prediction algorithm trRosetta, we developed QDistance, a new approach to the estimation of both global and local qualities. QDistance works for both single-model and multi-models inputs. We designed several distance-based features to assess the agreement between the predicted and model-derived inter-residue distances. Together with a few widely used features, they are fed into a simple yet powerful linear regression model to infer the global QA scores. The local QA scores for each structure model are predicted based on a comparative analysis with a set of selected reference models. For multi-models input, the reference models are selected from the input based on the predicted global QA scores. For single-model input, the reference models are predicted by trRosetta. With the informative distance-based features, QDistance can predict the global quality with satisfactory accuracy. Benchmark tests on the CASP13 and the CAMEO structure models suggested that QDistance was competitive other methods. Blind tests in the CASP14 experiments showed that QDistance was robust and ranked among the top predictors. Especially, QDistance was the top 3 local QA method and made the most accurate local QA prediction for unreliable local region. Analysis showed that this superior performance can be attributed to the inclusion of the predicted inter-residue distance.

AVAILABILITY AND IMPLEMENTATION

http://yanglab.nankai.edu.cn/QDistance.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The association between the vaginal microenvironment and fecundability: a register-based cohort study among Chinese women

OBJECTIVE

To evaluate the association between the vaginal microenvironment and fecundability among women.

DESIGN

Register-based nationwide cohort study.

SETTING

Chinese National Free Pre-conception Check-up Project from 2015 to 2018.

POPULATION

Our study included a total of 3 388 554 eligible women who were attempting to become pregnant.

METHOD

We assessed the vaginal microenvironment at baseline by considering four indices: vaginal pH, clue cell examination, whiff test and vaginal cleanliness grading. If any of these indicators was abnormal, the vaginal microenvironment was defined as poor. Propensity score matching was used to control for potential confounders and reduce bias. Logistic models were used to estimate the fecundability odds ratios (FORs) after adjustment for covariates.

MAIN OUTCOME MEASURES

Achievement of a pregnancy within 1 year.

RESULTS

Of the total study population, 379 718 women (11.2%) had a poor vaginal microenvironment and their pregnancy rate after 1 year was significantly lower than the group with a normal microenvironment (71.8% versus 76.1%, P < 0.001). After adjusting for potential confounders, the women with a poor vaginal microenvironment were associated with a 9% reduction in fecundability compared with the normal microenvironment group (FOR 0.91, 95% CI 0.90-0.92). The adverse effects of a poor vaginal microenvironment were stronger among multipara (FOR 0.89, 95% CI 0.87-0.90) or women with irregular menstruation (FOR 0.86, 95% CI 0.84-0.89).

CONCLUSION

There was a negative association between a poor vaginal microenvironment and the fecundability of women. These findings highlight the significance of assessing the vaginal microenvironment during pre-pregnancy health examinations.

TWEETABLE ABSTRACT

Women with a poor vaginal microenvironment were associated with a reduction in fecundability.

Disordered RNA-Binding Region Prediction with DisoRDPbind

RNA chaperone activity is one of the many functions of intrinsically disordered regions (IDRs). IDRs function without the prerequisite of a stable structure. Instead, their functions arise from structural ensembles. A common theme in IDR function is molecular recognition; IDRs mediate interactions with other proteins, RNA, and DNA. Many computational methods are available to predict IDRs from protein sequence, but relatively few are available for predicting IDR functions. Available methods primarily focus on protein-protein interactions. DisoRDPbind was developed to predict several protein functions including interactions with RNA. This method is available as a user-friendly web interface, located at http://biomine.cs.vcu.edu/servers/DisoRDPbind/ . The development and architecture of DisoRDPbind is briefly presented, and its accuracy relative to other RNA-binding residue predictors is discussed. We explain usage of the web interface in detail and provide an example of prediction results and interpretation. While DisoRDPbind does not identify RNA chaperones directly, we provide a case study of an RNA chaperone, HCV core protein, as an example of the method's utility in the study of RNA chaperones.

RNA inter-nucleotide 3D closeness prediction by deep residual neural networks

MOTIVATION

Recent years have witnessed that the inter-residue contact/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter-nucleotide 3D closeness.

RESULTS

We proposed a new algorithm named RNAcontact for the prediction of RNA inter-nucleotide 3D closeness. RNAcontact was built based on the deep residual neural networks. The covariance information from multiple sequence alignments and the predicted secondary structure were used as the input features of the networks. Experiments show that RNAcontact achieves the respective precisions of 0.8 and 0.6 for the top L/10 and L (where L is the length of an RNA) predictions on an independent test set, significantly higher than other evolutionary coupling methods. Analysis shows that about 1/3 of the correctly predicted 3D closenesses are not base pairings of secondary structure, which are critical to the determination of RNA structure. In addition, we demonstrated that the predicted 3D closeness could be used as distance restraints to guide RNA structure folding by the 3dRNA package. More accurate models could be built by using the predicted 3D closeness than the models without using 3D closeness.

AVAILABILITY AND IMPLEMENTATION

The webserver and a standalone package are available at: http://yanglab.nankai.edu.cn/RNAcontact/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Analysis of bone mineral density and its influencing factors in 211 patients with chronic hepatitis B treated with long-term entecavir monotherapy]

Objective: To investigate the changes of bone mineral density and its related influencing factors in chronic hepatitis B patients treated with long-term entecavir monotherapy. Methods: 211 cases with chronic hepatitis B treated with entecavir monotherapy in the Department of Infectious Diseases of Henan Provincial People's Hospital from June 2018 to September 2019 were retrospectively collected. Age, gender, body mass index, number of years of medication use, presence or absence of liver cirrhosis and current bone mineral density level (using dual-energy X-ray detection, taking lumbar L1 ~ 4 and left femur as observation region) and other related data were collected. 211 cases general situation was descriptively analyzed by case-control study design. Two independent sample t-tests were used to compare the differences in serum calcium, phosphorus, and renal function levels in patients with different medication durations. Univariate logistic regression was used to screen the influencing factors of bone mineral density level. Significant variables of univariate analysis were included in multivariate logistic regression to obtain the independent influencing factors leading to the decrease of bone mineral density level. The test level was set as α = 0.05. Results: The average age of 211 cases with chronic hepatitis B was (42.36 ± 11.10) years. The average medication time use was (2.52 ± 1.94) years. The body mass index (23.95 ± 3.11), and male-to-female ratio was 2.25/1. The incidence of liver cirrhosis was 35.5%. The incidence of low bone mass in the two observation sites (lumbar spine L1~4 and left femur) was 24.6% and 29.4%, respectively. There were statistically significant differences in serum calcium, phosphorus and renal function levels among patients with different entecavir treatment duration (≥3 years and < 3 years) (P < 0.05). Univariate analysis result showed that the influencing factors of BMD were age, the number of years of medication use, gender, liver cirrhosis (L1~4 of the lumbar spine region) and age, the number of years of medication, and gender (left femoral region). The variables that entered the two models after the multivariate analysis were age (L1~4 region of lumbar spine: OR = 2.225, left femur OR = 1.660), gender (L1~4 region of lumbar spine: OR = 3.048, left femur OR = 2.496), number of years of medication use (L1~4 region of lumbar spine: OR = 1.387, left femur OR = 1.276). Conclusion: Age, gender, and the number of years of medication use are independent factors that influence the bone mineral density of patients with chronic hepatitis B treated with long-term entecavir. Low bone mass risk at the two observation sites is 2.225 and 1.66 times the normal level for every 10 years of age increase. Compared with men, the risk of low bone mass at the two observation sites is 3.048 and 2.496 times for women, and for every additional year of medication use, the risk of low bone mass at the two observation sites is 1.387 and 1.276 times the normal level. Female patients with older age and prolonged medication use are at high risk of developing bone mineral density reduction.

APOD: accurate sequence-based predictor of disordered flexible linkers

Motivation

Disordered flexible linkers (DFLs) are abundant and functionally important intrinsically disordered regions that connect protein domains and structural elements within domains and which facilitate disorder-based allosteric regulation. Although computational estimates suggest that thousands of proteins have DFLs, they were annotated experimentally in <200 proteins. This substantial annotation gap can be reduced with the help of accurate computational predictors. The sole predictor of DFLs, DFLpred, trade-off accuracy for shorter runtime by excluding relevant but computationally costly predictive inputs. Moreover, it relies on the local/window-based information while lacking to consider useful protein-level characteristics.

Results

We conceptualize, design and test APOD (Accurate Predictor Of DFLs), the first highly accurate predictor that utilizes both local- and protein-level inputs that quantify propensity for disorder, sequence composition, sequence conservation and selected putative structural properties. Consequently, APOD offers significantly more accurate predictions when compared with its faster predecessor, DFLpred, and several other alternative ways to predict DFLs. These improvements stem from the use of a more comprehensive set of inputs that cover the protein-level information and the application of a more sophisticated predictive model, a well-parametrized support vector machine. APOD achieves area under the curve = 0.82 (28% improvement over DFLpred) and Matthews correlation coefficient = 0.42 (180% increase over DFLpred) when tested on an independent/low-similarity test dataset. Consequently, APOD is a suitable choice for accurate and small-scale prediction of DFLs.

Availability and implementation

https://yanglab.nankai.edu.cn/APOD/.

Recognition of small molecule-RNA binding sites using RNA sequence and structure

Motivation

RNA molecules become attractive small molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule–RNA binding sites.

Results

We developed a novel method RNAsite to predict small molecule–RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be competitive with the state-of-the-art methods on the experimental structures of two independent test sets. When predicted structure models were used, RNAsite outperforms other methods by a large margin. The possibility of improving RNAsite by geometry-based binding pocket detection was investigated. The influence of RNA structure’s flexibility and the conformational changes caused by ligand binding on RNAsite were also discussed. RNAsite is anticipated to be a useful tool for the design of RNA-targeting small molecule drugs.

Availability and implementation

http://yanglab.nankai.edu.cn/RNAsite.

Supplementary information

Supplementary data are available at Bioinformatics online.

APOD: accurate sequence-based predictor of disordered flexible linkers

MOTIVATION

Disordered flexible linkers (DFLs) are abundant and functionally important intrinsically disordered regions that connect protein domains and structural elements within domains and which facilitate disorder-based allosteric regulation. Although computational estimates suggest that thousands of proteins have DFLs, they were annotated experimentally in <200 proteins. This substantial annotation gap can be reduced with the help of accurate computational predictors. The sole predictor of DFLs, DFLpred, trade-off accuracy for shorter runtime by excluding relevant but computationally costly predictive inputs. Moreover, it relies on the local/window-based information while lacking to consider useful protein-level characteristics.

RESULTS

We conceptualize, design and test APOD (Accurate Predictor Of DFLs), the first highly accurate predictor that utilizes both local- and protein-level inputs that quantify propensity for disorder, sequence composition, sequence conservation and selected putative structural properties. Consequently, APOD offers significantly more accurate predictions when compared with its faster predecessor, DFLpred, and several other alternative ways to predict DFLs. These improvements stem from the use of a more comprehensive set of inputs that cover the protein-level information and the application of a more sophisticated predictive model, a well-parametrized support vector machine. APOD achieves area under the curve = 0.82 (28% improvement over DFLpred) and Matthews correlation coefficient = 0.42 (180% increase over DFLpred) when tested on an independent/low-similarity test dataset. Consequently, APOD is a suitable choice for accurate and small-scale prediction of DFLs.

AVAILABILITY AND IMPLEMENTATION

https://yanglab.nankai.edu.cn/APOD/.

[Impacts of incomplete revascularization following off-pump coronary artery bypass grafting on perioperative outcomes in octogenarians]

Objective: To assess the impacts of incomplete revascularization following off-pump coronary artery bypass grafting (OPCABG) on perioperative outcomes in octogenarians. Methods: A retrospective analysis of 242 octogenarian patients with coronary artery disease (CAD) hospitalized in Beijing Anzhen Hospital from June 2008 to July 2016 was performed. These patients were divided into the complete revascularization group (n=181) and the incomplete revascularization group (n=61) depending on whether they underwent complete revascularization. The impacts of incomplete revascularization following OPCABG on perioperative outcomes were summarized and compared between the two groups. Results: Among the 242 patients over 80 years who received OPCABG, there were 198 males (81.8%). Compared to the complete revascularization group, those in the incomplete revascularization group were older [(83.2±1.5) vs (81.5±1.1) years old, P=0.03], with more carotid stenosis (44.3% vs 25.4%, P=0.01), more involved in the diagonal and circumflex branch of coronary artery (49.2% vs 17.1%, P=0.01; 83.6% vs 70.2%, P=0.03), shorter operative time [(4.1±1.7) h vs (4.7±1.2) h, P=0.03), longer preoperative [(7.1±2.3) d vs (5.2±2.0) d, P=0.01] and total hospitalization time [(16.3±6.8) d vs (12.5±4.2) d, P=0.01], however, the differences of the in-hospital mortality and incidence of other perioperative complications were not statistically significant between the two groups (all P>0.05). Conclusion: Compared with complete revascularization, incomplete revascularization following OPCABG in CAD patients over 80 years old does not increase the perioperative mortality and the incidence of other complications, and it reduces the operative time. However, it increases the time of preoperative and total hospital stay.

CATHER: a novel threading algorithm with predicted contacts

MOTIVATION

Threading is one of the most effective methods for protein structure prediction. In recent years, the increasing accuracy in protein contact map prediction opens a new avenue to improve the performance of threading algorithms. Several preliminary studies suggest that with predicted contacts, the performance of threading algorithms can be improved greatly. There is still much room to explore to make better use of predicted contacts.

RESULTS

We have developed a new contact-assisted threading algorithm named CATHER using both conventional sequential profiles and contact map predicted by a deep learning-based algorithm. Benchmark tests on an independent test set and the CASP12 targets demonstrated that CATHER made significant improvement over other methods which only use either sequential profile or predicted contact map. Our method was ranked at the Top 10 among all 39 participated server groups on the 32 free modeling targets in the blind tests of the CASP13 experiment. These data suggest that it is promising to push forward the threading algorithms by using predicted contacts.

AVAILABILITY AND IMPLEMENTATION

http://yanglab.nankai.edu.cn/CATHER/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[A survey on therapy strategies for rheumatoid arthritis in Chinese rheumatologists]

To investigate how Chinese rheumatologists treated patients with rheumatoid arthritis (RA). We performed a survey on the choices of first-line and second-line anti-RA therapies, prescription of methotrexate and glucocorticoids, assessment of disease activity and frequencies of follow-up at the Asia Pacific League of Associations for Rheumatology meeting 2016 in Shanghai. The majority (85.1%) of rheumatologists preferred methotrexate as first-line treatment. As alternative agents, 71.0% rheumatologists chose leflunomide or sulfasalazine. If methotrexate was not tolerable, only 8.6% rheumatologists would switch to parenteral administration. After failure of responding to methotrexate, 62.0% rheumatologists recommended to change or combine other conventional synthetic disease modifying anti-rheumatic drugs (DMARDs). Etanercept was the most popular biological option in 65.2% rheumatologists. Almost all (97.3%) rheumatologists prescribed methotrexate at an initial dose of 7.5 to 15 mg/week and 73.8% rheumatologists at a maximum of 10 to 15 mg/week. There were 49.3% rheumatologists prescribing oral glucocorticoids at first-line therapy. Surprisingly, 42.6% rheumatologists never or rarely assessed disease activity in daily work. For patients having achieved remission, 74.2% rheumatologists would follow up them every 1 to 3 months. This study suggests that most Chinese rheumatologists treat RA patients consistent with international guidelines, while the maximum dose of methotrexate, glucocorticoid as first-line treatment, assessment of disease activity and follow-up frequency are locally modified.