Prevalence, Risk Factors, and Metabolic Characteristics of Metabolically Healthy Obesity in Patients Seeking Bariatric Surgery: A Cohort Study

BACKGROUND

Bariatric surgery is an effective treatment for morbid obesity. However, a subset of individuals seeking bariatric surgery may exhibit a metabolically healthy obesity (MHO) phenotype, suggesting that they may not experience metabolic complications despite being overweight.

OBJECTIVE

This study aimed to determine the prevalence and metabolic features of MHO in a population undergoing bariatric surgery.

METHODS

A representative sample of 665 participants aged 14 or older who underwent bariatric surgery at our center from January 1, 2010 to January 1, 2020 was included in this cohort study. MHO was defined based on specific criteria, including blood pressure, waist-to-hip ratio, and absence of diabetes.

RESULTS

Among the 665 participants, 80 individuals (12.0%) met the criteria for MHO. Female gender (P = .021) and younger age (P < .001) were associated with a higher likelihood of MHO. Smaller weight and BMI were observed in individuals with MHO. However, a considerable proportion of those with MHO exhibited other metabolic abnormalities, such as fatty liver (68.6%), hyperuricemia (55.3%), elevated lipid levels (58.7%), and abnormal lipoprotein levels (88%).

CONCLUSION

Approximately 1 in 8 individuals referred for bariatric surgery displayed the phenotype of MHO. Despite being metabolically healthy based on certain criteria, a significant proportion of individuals with MHO still exhibited metabolic abnormalities, such as fatty liver, hyperuricemia, elevated lipid levels, and abnormal lipoprotein levels, highlighting the importance of thorough metabolic evaluation in this population.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Comparison of various prediction models in the effect of laparoscopic sleeve gastrectomy on type 2 diabetes mellitus in the Chinese population 5 years after surgery

BACKGROUND

The effect of bariatric surgery on type 2 diabetes mellitus (T2DM) control can be assessed based on predictive models of T2DM remission. Various models have been externally verified internationally. However, long-term validated results after laparoscopic sleeve gastrectomy (LSG) surgery are lacking. The best model for the Chinese population is also unknown.

METHODS

We retrospectively analyzed Chinese population data 5 years after LSG at Beijing Shijitan Hospital in China between March 2009 and December 2016. The independent t -test, Mann-Whitney U test, and chi-squared test were used to compare characteristics between T2DM remission and non-remission groups. We evaluated the predictive efficacy of each model for long-term T2DM remission after LSG by calculating the area under the curve (AUC), sensitivity, specificity, Youden index, positive predictive value (PPV), negative predictive value (NPV), and predicted-to-observed ratio, and performed calibration using Hosmer-Lemeshow test for 11 prediction models.

RESULTS

We enrolled 108 patients, including 44 (40.7%) men, with a mean age of 35.5 years. The mean body mass index was 40.3 ± 9.1 kg/m 2 , the percentage of excess weight loss (%EWL) was (75.9 ± 30.4)%, and the percentage of total weight loss (%TWL) was (29.1± 10.6)%. The mean glycated hemoglobin A1c (HbA1c) level was (7.3 ± 1.8)% preoperatively and decreased to (5.9 ± 1.0)% 5 years after LSG. The 5-year postoperative complete and partial remission rates of T2DM were 50.9% [55/108] and 27.8% [30/108], respectively. Six models, i.e., "ABCD", individualized metabolic surgery (IMS), advanced-DiaRem, DiaBetter, Dixon et al' s regression model, and Panunzi et al 's regression model, showed a good discrimination ability (all AUC >0.8). The "ABCD" (sensitivity, 74%; specificity, 80%; AUC, 0.82 [95% confidence interval [CI]: 0.74-0.89]), IMS (sensitivity, 78%; specificity, 84%; AUC, 0.82 [95% CI: 0.73-0.89]), and Panunzi et al' s regression models (sensitivity, 78%; specificity, 91%; AUC, 0.86 [95% CI: 0.78-0.92]) showed good discernibility. In the Hosmer-Lemeshow goodness-of-fit test, except for DiaRem ( P <0.01), DiaBetter ( P <0.01), Hayes et al ( P = 0.03), Park et al ( P = 0.02), and Ramos-Levi et al' s ( P <0.01) models, all models had a satifactory fit results ( P >0.05). The P values of calibration results of the "ABCD" and IMS were 0.07 and 0.14, respectively. The predicted-to-observed ratios of the "ABCD" and IMS were 0.87 and 0.89, respectively.

CONCLUSION

The prediction model IMS was recommended for clinical use because of excellent predictive performance, good statistical test results, and simple and practical design features.

Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data

Leveraging iterative alignment search through genomic and metagenome sequence databases, we report the DeepMSA2 pipeline for uniform protein single- and multichain multiple-sequence alignment (MSA) construction. Large-scale benchmarks show that DeepMSA2 MSAs can remarkably increase the accuracy of protein tertiary and quaternary structure predictions compared with current state-of-the-art methods. An integrated pipeline with DeepMSA2 participated in the most recent CASP15 experiment and created complex structural models with considerably higher quality than the AlphaFold2-Multimer server (v.2.2.0). Detailed data analyses show that the major advantage of DeepMSA2 lies in its balanced alignment search and effective model selection, and in the power of integrating huge metagenomics databases. These results demonstrate a new avenue to improve deep learning protein structure prediction through advanced MSA construction and provide additional evidence that optimization of input information to deep learning-based structure prediction methods must be considered with as much care as the design of the predictor itself.

Integrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15

We report the results of the "UM-TBM" and "Zheng" groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D-I-TASSER and DMFold-Multimer algorithms, respectively. For monomer structure prediction, D-I-TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi-source MSA searching and a structural modeling-based MSA ranker; (ii) attention-network based spatial restraints; (iii) a multi-domain module containing domain partition and arrangement for domain-level templates and spatial restraints; (iv) an optimized I-TASSER-based folding simulation system for full-length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge-based potentials. For 47 free modeling targets in CASP15, the final models predicted by D-I-TASSER showed average TM-score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo-based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end-to-end deep learning methods alone. For protein complex structure prediction, DMFold-Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end-to-end modeling module from AlphaFold2-Multimer. For the 38 complex targets, DMFold-Multimer generated models with an average TM-score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking.

Comparison of various prediction models in the effect of Roux-en-Y gastric bypass on type 2 diabetes in the Chinese population 5 years after surgery

BACKGROUND

Various prediction models of type 2 diabetes (T2D) remission have been externally verified internationally. However, long-term validated results after Roux-en-Y gastric bypass (RYGB) surgery are lacking. The best model for the Chinese population is also unknown.

OBJECTIVES

To evaluate the prediction effect of various prediction models on the long-term diabetes remission after RYGB in the Chinese population and to provide reference for clinical use.

SETTING

A retrospective clinical study at a university hospital.

METHODS

We retrospectively analyzed Chinese population data 5 years after RYGB and externally validated 11 predictive models to evaluate the predictive effect of each model on long-term T2D remission after RYGB.

RESULTS

We enrolled 84 patients. The mean body mass index was 41 kg/m2, and the percentage of excess weight loss (%EWL) was 72.3%. The mean glycated hemoglobin level was 8.4% preoperatively and decreased to 5.9% after 5 years. The 5-year postoperative complete and partial remission rates of T2D were 31% and 70.2%, respectively. The ABCD scoring model (sensitivity, 84%; specificity, 76%; area under the curve [AUC], .866) and the Panuzi et al. [34] study (sensitivity, 84%; specificity, 81%; AUC, .842) showed excellent results. In the Hosmer-Lemeshow goodness-of-fit test, calibration values for ABCD and Panuzi et al. [34] were .14 and .21, respectively. The predicted-to-observed ratios of ABCD and Panuzi et al. [34] were .83 and .88, respectively.

CONCLUSIONS

T2D was relieved to varying degrees 5 years after RYGB in patients with obesity. The prediction models in ABCD and the Panuzi et al. [34] studies showed the best prediction effects. ABCD was recommended for clinical use because of excellent predictive performance, good statistical test results, and simple and practical design features.

Analysis of Correlation between Age and Satisfied Total Weight Loss Percentage Outcome at 1 Year after Bariatric Surgery using the Restricted Cubic Spline Function: A Retrospective Study in China

OBJECTIVE

This study aims to explore the relationship between age and whether the percentage of total weight loss (TWL%) is ≥ 25% or not at 1 year after bariatric surgery (BS). We aimed to provide evidence for the stratified treatment of spatients with obesity at different ages.

METHODS

The primary outcome evaluated was whether TWL% was no less than 25% at 1 year after BS. A TWL% ≥ 25% was defined as a satisfied TWL% outcome. Logistic regression analysis and the restricted cubic spline (RCS) function were used to analyze the relationship between age and the satisfied TWL% outcome at 1 year after BS.

RESULTS

Two hundred and ninety-one patients were included in our study. After adjusting for potential confounders, the odds ratios (ORs) of the corresponding quartiles of age associated with satisfied TWL% outcome were 1.00 (reference), 1.117 [95% confidence interval (95% CI) = 0.540-2.311], 1.378 (95% CI = 0.647-2.935), and 0.406 (95% CI = 0.184-0.895). RCS analysis revealed a non-linear inverted L-shaped association between age and satisfied TWL% outcome at 1 year after BS (non-linear P = 0.033).

CONCLUSION

Age was an independent predictor of satisfied TWL% outcome one year following BS, and our study considered 32 years as a potential cut-off point. For Chinese patients over the age of 32 who are eligible for BS, it may be beneficial to do BS earlier as the probability of achieving a satisfied TWL% outcome may decrease with age.

A nomogram based on adipogenesis-related methylation sites in intraoperative visceral fat to predict EWL% at 1 year following laparoscopic sleeve gastrectomy

BACKGROUND

Laparoscopic sleeve gastrectomy (LSG) is a crucial surgical procedure for patients with obesity. However, epigenetic research in LSG is still in its infancy from the perspective of adipogenesis.

OBJECTIVES

This work aims to develop a model to predict 1 year excess weight loss percentage (EWL)% following LSG in Chinese patients with obesity by examining the DNA methylation profiles of intraoperative visceral fat.

SETTING

University hospital, Beijing, China.

METHODS

Firstly, we classified patients with obesity as either the satisfied group or unsatisfied group depending on whether their EWL% was 50% or higher at 1 year following LSG. After that, we analyzed differentially methylated sites (DMSs) between the satisfied group and unsatisfied group. DMSs were mapped to the corresponding differentially methylated genes. Then, we took the intersection of adipogenesis-related genes and differentially methylated genes and obtained adipogenesis-related DMSs. Next, hub methylation sites were identified by least absolute shrinkage and selection operator analysis. Finally, a nomogram was developed to predict EWL% of Chinese patients with obesity at 1 -year following LSG.

RESULTS

A total of 26 patients with obesity were enrolled in the study, including 13 in the satisfied group and 13 in the unsatisfied group. A total of 16 genes and 31 DMSs were involved in the adipogenesis signaling pathway. Finally, 4 hub methylation sites (cg06093355, cg00294552, cg00753924, and cg17092065) were identified and a predictive nomogram was established.

CONCLUSIONS

The predictive nomogram based on methylation sites including cg06093355, cg00294552, cg00753924, and cg17092065 can predict EWL% at 1 year following LSG in Chinese patients with obesity efficiently.

Long-term weight loss outcome of laparoscopic Roux-en-Y gastric bypass predicted by weight loss at 6 months in Chinese patients with BMI ≥ 32.5 kg/m2

Laparoscopic Roux-en-Y gastric bypass (LRYGB) is classic bariatric procedure with long-term safety and efficacy. However, no studies have focused on predicting long-term weight loss after LRYGB in Chinese patients with body mass index (BMI) ≥ 32.5 kg/m2. To explore the relationship between initial and long-term weight loss after LRYGB in patients with BMI ≥ 32.5 kg/m2. All patients were followed-up to evaluate BMI, percentage of excess weight loss (%EWL), and comorbidities. Linear and logistic regression were performed to assess the relationship between initial and long-term weight loss. Receiver operating characteristic curve was used to determine optimal cutoff value. We enrolled 104 patients. The median preoperative BMI was 41.44 (37.92-47.53) kg/m2. %EWL ≥ 50% at 5 years was considered as successful weight loss, and 75.00% of the patients successfully lost weight. The cure rates of hypertension, hyperlipidemia, and type 2 diabetes mellitus at 1 year were 84.38%, 33.93%, and 60.82%, respectively. %EWL at 6 months and 5 years were positively correlated and its relationship could be described by following linear equation: %EWL5 years = 43.934 + 0.356 × %EWL6 months (P < .001; r2 = 0.166). The best cutoff %EWL at 6 months after LRYGB to predict 5-year successful weight loss was 63.93% (sensitivity, 53.85%; specificity, 84.62%; area under the curve (AUC) = 0.671). In Chinese patients with BMI ≥ 32.5 kg/m2, %EWL at 6 months and 5 years were positively correlated and %EWL at 5 years could be calculated by following linear equation: %EWL5 years = 43.934 + 0.356 × %EWL6 months.

An Assessment of the Effect of Bariatric Surgery on Cardiovascular Disease Risk in the Chinese Population Using Multiple Cardiovascular Risk Models

BACKGROUND

Many studies have reported that bariatric surgery may reduce postoperative cardiovascular risk in patient with obesity, but few have addressed this risk in the Chinese population.

OBJECTIVE

To assess the impact of bariatric surgery on cardiovascular disease (CVD) risk in the Chinese population using the World Health Organization (WHO) risk model, the Global risk model, and the Framingham Risk Score.

METHODS

We retrospectively analyzed data collected on patient with obesity who underwent bariatric surgery at our institution between March 2009 and January 2021. Their demographic characteristics, anthropometric variables, and glucolipid metabolic parameters were assessed preoperatively and at their 1-year postoperative follow-up. Subgroup analysis compared body mass index (BMI) < 35 kg/m² and BMI ≥ 35 kg/m², as well as gender. We used the 3 models to calculate their CVD risk.

RESULTS

We evaluated 61 patients, of whom 26 (42.62%) had undergone sleeve gastrectomy (SG) surgery and 35 (57.38%) Roux-en-Y gastric bypass (RYGB) surgery. Of the patients with BMI ≥ 35 kg/m², 66.67% underwent SG, while 72.97% with BMI < 35 kg/m² underwent RYGB. HDL levels were significantly higher at 12 months postoperatively relative to baseline. When the models were applied to calculate CVD risk in Chinese patients with obesity, the 1-year CVD risk after surgery were reduced lot compared with the preoperative period.

CONCLUSION

Patient with obesity had significantly lower CVD risks after bariatric surgery. This study also demonstrates that the models are reliable clinical tools for assessing the impact of bariatric surgery on CVD risk in the Chinese population.

Establishment of a Nomogram Based on Inflammatory Response-Related Methylation Sites in Intraoperative Visceral Adipose Tissue to Predict EWL% at One Year After LSG

Background

Laparoscopic sleeve gastrectomy (LSG) is considered as an effective bariatric and metabolic surgery for patients with severe obesity. Chronic low-grade inflammation of adipose tissue is associated with obesity and obesity-related complications.

Objective

This study intends to establish a nomogram based on inflammatory response-related methylation sites in intraoperative visceral adipose tissue (VAT) to predict excess weight loss (EWL)% at one-year after LSG.

Methods

Based on EWL% at one-year after LSG, patients were divided into two groups: the satisfied group (group-A, EWL%≥50%) and the unsatisfied group (group-B, EWL%<50%). Next, we defined genes corresponding to the methylation sites in the 850 K methylation microarray as methylation-related genes (MRGs). We then took the intersection of MRGs and inflammatory response-related genes. After that, inflammatory response-related methylation sites were identified based on overlapping genes. Moreover, difference analysis was carried out to obtain inflammatory response-related differentially methylated sites (IRRDMSs) between group-A and group-B. LASSO analysis was used to identify the hub methylation sites. Finally, we developed a nomogram based on the hub methylation sites.

Results

There were 26 patients in the study, with 13 in group-A and 13 in group-B. After data filtering and difference analysis, 200 IRRDMSs were identified (143 hypermethylated sites and 57 hypomethylated sites). Then, we identified three hub methylation sites (cg03610073, cg03208951, and cg18746357) by LASSO analysis and built a predictive nomogram (Area under the curve=0.953).

Conclusion

The predictive nomogram based on three inflammatory-related methylation sites (cg03610073, cg03208951, and cg18746357) in intraoperative visceral adipose tissue can predict one-year EWL% after LSG effectively.

The long non-coding RNA T cell leukemia homeobox 1 neighbor enhances signal transducer and activator of transcription 5A phosphorylation to promote colon cancer cell invasion, migration, and metastasis

Colon cancer is among the most prevalent gastrointestinal tumor types. The long noncoding RNA (lncRNA) T cell leukemia homeobox 1 neighbor (TLX1NB) is up-regulated in colorectal cancer (CRC). However, the functional role of this lncRNA in colon cancer remains unknown. In our study, we investigated the clinical significance of TLX1NB in colon cancer through bioinformatics analysis and explored its role in migration, invasion and metastasis of colon cancer cell with a series of experiments. Firstly, TLX1NB was up-regulated in colon cancer tissues and increased TLX1NB expression was significantly associated with advanced N stages. In wound healing assays and transwell assays, TLX1NB overexpression promoted HCT116 cell migration and invasion while TLX1NB knockdown inhibited SW620 cell migration and invasion. In vivo, TLX1NB knockdown suppressed pulmonary metastasis of SW620 cell and vimentin expression but increased E-cadherin expression. Then, TLX1NB overexpression enhanced signal transducer and activator of transcription 5A (STAT5A) phosphorylation and TLX1NB knockdown suppressed STAT5A phosphorylation. Moreover, the inhibition of STAT5A phosphorylation reversed TLX1NB overexpression-associated increase in HCT116 cell migratory and invasive activity. In conclusion, TLX1NB enhances STAT5A phosphorylation to promote colon cancer cell invasion, migration, and metastasis.

LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation

Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep learning threading methods. For the first time, we have extended LOMETS3 to handle multi-domain proteins and to construct full-length models with gradient-based optimizations. Starting from a FASTA-formatted sequence, LOMETS3 performs four steps of domain boundary prediction, domain-level template identification, full-length template/model assembly and structure-based function prediction. The output of LOMETS3 contains (i) top-ranked templates from LOMETS3 and its component threading programs, (ii) up to 5 full-length structure models constructed by L-BFGS (limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm) optimization, (iii) the 10 closest Protein Data Bank (PDB) structures to the target, (iv) structure-based functional predictions, (v) domain partition and assembly results, and (vi) the domain-level threading results, including items (i)–(iii) for each identified domain. LOMETS3 was tested in large-scale benchmarks and the blind CASP14 (14th Critical Assessment of Structure Prediction) experiment, where the overall template recognition and function prediction accuracy is significantly beyond its predecessors and other state-of-the-art threading approaches, especially for hard targets without homologous templates in the PDB. Based on the improved developments, LOMETS3 should help significantly advance the capability of broader biomedical community for template-based protein structure and function modelling.

Long-Term Weight Loss Outcome of Laparoscopic Sleeve Gastrectomy Predicted by the Percentage of Excess Weight Loss at 6 Months in Chinese Patients with Body Mass Index ≥ 32.5 Kg/m2

PURPOSE

To evaluate the predictive effect of the initial weight loss on the long-term weight loss in Chinese patients with a body mass index (BMI) ≥ 32.5 kg/m² who underwent LSG.

PATIENTS AND METHODS

The follow-up was completed via phone or WeChat for outpatients and at the hospital for inpatients. We evaluated the BMI, percentage of excess weight loss (%EWL), and type 2 diabetes mellitus, hypertension, and hyperlipidemia statuses. Linear and logistic regression analyses were performed on the relationship between the initial and long-term weight loss. The optimal cut-off value was determined by receiver operating characteristic (ROC) curve analysis.

RESULTS

We enrolled 307 patients, with a median preoperative BMI of 39.68 (35.68, 45.47) kg/m². %EWL ≥ 50% was regarded as successful weight loss, and 76.55% of the patients lost their weight successfully. (Reviewer #1, comment #4) %EWL at 6 months and 5 years were positively correlated (P < 0.001). Further, the following linear equation could express the relationship: (%EWL_{5 years} = 29.193 + 0.526 × %EWL_{6 months}). %EWL ≥ 58.57% at 6 months was the best predictor of successful weight loss at 5 years after LSG (Reviewer #1, comment #5) (sensitivity, 73.62%; specificity, 73.61%; AUC value, 0.780). Internal verification of the prediction model revealed satisfactory results in terms of discrimination and calibration.

CONCLUSION

In Chinese patients with BMI ≥ 32.5 kg/m² who underwent LSG, %EWL at 6 months and 5 years were correlated. %EWL ≥ 58.57% at 6 months was a predictor of successful long-term weight loss.

Retrospective Comparison of SADI-S Versus RYGB in Chinese with Diabetes and BMI< 35kg/m2: a Propensity Score Adjustment Analysis

BACKGROUND

As a modification of the duodenal switch (DS), single-anastomosis duodenal-ileal bypass with sleeve gastrectomy (SADI-S) has recently become very popular and is successful for weight loss and T2DM remission. However, current studies have been mostly aimed at patients with severe obesity.

OBJECTIVES

In this study, we firstly compare primary SADI-S to the Roux-en-Y gastric bypass (RYGB) in Chinese with diabetes and BMI< 35 kg/m².

METHODS

Using a propensity score (PS) matching analysis, we analyzed all patients with diabetes and BMI< 35 kg/m² who underwent primary SADI-S or RYGB. All surgeries were conducted by a single surgeon at a Chinese center from June 2017 to January 2019.

RESULTS

Twenty-six patients who underwent SADI-S and 65 patients who underwent RYGB were included in our analysis. Of these, 26 (100%) of patients in the SADI-S group and 43 (66%) of patients in the RYGB group completed the 24-month follow-up. No severe perioperative complication was observed in either group. There was a statistically higher percentage of total weight loss with SADI-S at the 2-year follow-up when compared to RYGB (p = 0.017 after PS correction). After PS adjustment, 76.5% of patients in the SADI-S group and 82.4% of patients in the RYGB group achieved complete remission of T2DM (p = 1.000). Nutritional outcomes were similar in the two groups.

CONCLUSION

In Chinese with diabetes and BMI< 35 kg/m², with comparable T2DM remission and nutritional outcomes, primary SADI-S allows for better weight loss than RYGB. Compared with RYGB, SADI-S is also a safe, effective, and feasible treatment for these patients.

Disentangling Population History and Character Evolution among Hybridizing Lineages

Understanding the origin and maintenance of adaptive phenotypic novelty is a central goal of evolutionary biology. However, both hybridization and incomplete lineage sorting can lead to genealogical discordance between the regions of the genome underlying adaptive traits and the remainder of the genome, decoupling inferences about character evolution from population history. Here, to disentangle these effects, we investigated the evolutionary origins and maintenance of Batesian mimicry between North American admiral butterflies (Limenitis arthemis) and their chemically defended model (Battus philenor) using a combination of de novo genome sequencing, whole-genome resequencing, and statistical introgression mapping. Our results suggest that balancing selection, arising from geographic variation in the presence or absence of the unpalatable model, has maintained two deeply divergent color patterning haplotypes that have been repeatedly sieved among distinct mimetic and nonmimetic lineages of Limenitis via introgressive hybridization.

FUpred: detecting protein domains through deep-learning-based contact map prediction

MOTIVATION

Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence.

RESULTS

We developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural networks coupled with coevolutionary precision matrices. The core idea of the algorithm is to retrieve domain boundary locations by maximizing the number of intra-domain contacts, while minimizing the number of inter-domain contacts from the contact maps. FUpred was tested on a large-scale dataset consisting of 2549 proteins and generated correct single- and multi-domain classifications with a Matthew's correlation coefficient of 0.799, which was 19.1% (or 5.3%) higher than the best machine learning (or threading)-based method. For proteins with discontinuous domains, the domain boundary detection and normalized domain overlapping scores of FUpred were 0.788 and 0.521, respectively, which were 17.3% and 23.8% higher than the best control method. The results demonstrate a new avenue to accurately detect domain composition from sequence alone, especially for discontinuous, multi-domain proteins.

AVAILABILITY AND IMPLEMENTATION

https://zhanglab.ccmb.med.umich.edu/FUpred.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins

The LOMETS2 server (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is an online meta-threading server system for template-based protein structure prediction. Although the server has been widely used by the community over the last decade, the previous LOMETS server no longer represents the state-of-the-art due to aging of the algorithms and unsatisfactory performance on distant-homology template identification. An extension of the server built on cutting-edge methods, especially techniques developed since the recent CASP experiments, is urgently needed. In this work, we report the recent advancements of the LOMETS2 server, which comprise a number of major new developments, including (i) new state-of-the-art threading programs, including contact-map-based threading approaches, (ii) deep sequence search-based sequence profile construction and (iii) a new web interface design that incorporates structure-based function annotations. Large-scale benchmark tests demonstrated that the integration of the deep profiles and new threading approaches into LOMETS2 significantly improve its structure modeling quality and template detection, where LOMETS2 detected 176% more templates with TM-scores >0.5 than the previous LOMETS server for Hard targets that lacked homologous templates. Meanwhile, the newly incorporated structure-based function prediction helps extend the usefulness of the online server to the broader biological community.

An Application of Random Walk Resampling to Phylogenetic HMM Inference and Learning

Statistical resampling methods are widely used for confidence interval placement and as a data perturbation technique for statistical inference and learning. An important assumption of popular resampling methods such as the standard bootstrap is that input observations are identically and independently distributed (i.i.d.). However, within the area of computational biology and bioinformatics, many different factors can contribute to intra-sequence dependence, such as recombination and other evolutionary processes governing sequence evolution. The SEquential RESampling ("SERES") framework was previously proposed to relax the simplifying assumption of i.i.d. input observations. SERES resampling takes the form of random walks on an input of either aligned or unaligned biomolecular sequences. This study introduces the first application of SERES random walks on aligned sequence inputs and is also the first to demonstrate the utility of SERES as a data perturbation technique to yield improved statistical estimates. We focus on the classical problem of recombination-aware local genealogical inference. We show in a simulation study that coupling SERES resampling and re-estimation with recHMM, a hidden Markov model-based method, produces local genealogical inferences with consistent and often large improvements in terms of topological accuracy. We further evaluate method performance using empirical HIV genome sequence datasets.

Two-Level Protein Methylation Prediction using structure model-based features

Protein methylation plays a vital role in cell processing. Many novel methods try to predict methylation sites from protein sequence by sequence information or predicted structural information, but none of them use protein tertiary structure information in prediction. In particular, most of them do not build models for predicting methylation types (mono-, di-, tri-methylation). To address these problems, we propose a novel method, Met-predictor, to predict methylation sites and methylation types using a support vector machine-based network. Met-predictor combines a variety of sequence-based features that are derived from protein sequences with structure model-based features, which are geometric information extracted from predicted protein tertiary structure models, and are firstly used in methylation prediction. Met-predictor was tested on two independent test sets, where the addition of structure model-based features improved AUC from 0.611 and 0.520 to 0.655 and 0.566 for lysine and from 0.723 and 0.640 to 0.734 and 0.643 for arginine. When compared with other state-of-the-art methods, Met-predictor had 13.1% (3.9%) and 8.5% (16.4%) higher accuracy than the best of other methods for methyllysine and methylarginine prediction on the independent test set I (II). Furthermore, Met-predictor also attains excellent performance for predicting methylation types.

Detecting distant-homology protein structures by aligning deep neural-network based contact maps

Accurate prediction of atomic-level protein structure is important for annotating the biological functions of protein molecules and for designing new compounds to regulate the functions. Template-based modeling (TBM), which aims to construct structural models by copying and refining the structural frameworks of other known proteins, remains the most accurate method for protein structure prediction. Due to the difficulty in recognizing distant-homology templates, however, the accuracy of TBM decreases rapidly when the evolutionary relationship between the query and template vanishes. In this study, we propose a new method, CEthreader, which first predicts residue-residue contacts by coupling evolutionary precision matrices with deep residual convolutional neural-networks. The predicted contact maps are then integrated with sequence profile alignments to recognize structural templates from the PDB. The method was tested on two independent benchmark sets consisting collectively of 1,153 non-homologous protein targets, where CEthreader detected 176% or 36% more correct templates with a TM-score >0.5 than the best state-of-the-art profile- or contact-based threading methods, respectively, for the Hard targets that lacked homologous templates. Moreover, CEthreader was able to identify 114% or 20% more correct templates with the same Fold as the query, after excluding structures from the same SCOPe Superfamily, than the best profile- or contact-based threading methods. Detailed analyses show that the major advantage of CEthreader lies in the efficient coupling of contact maps with profile alignments, which helps recognize global fold of protein structures when the homologous relationship between the query and template is weak. These results demonstrate an efficient new strategy to combine ab initio contact map prediction with profile alignments to significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

Despite decades of effort in computational method development, template-based modeling (TBM) still remains the most reliable approach to high-resolution protein structure prediction. Previous studies have shown that the PDB library is complete for single-domain proteins and TBM is in principle sufficient to solve the structure prediction problem if the most similar structure in the PDB could be reliably identified and used as template for model reconstruction. But in reality, the success of TBM depends on the availability of closely-homologous templates, where its accuracy and reliability decrease sharply when the evolutionary relationship between query and template becomes more distant. We developed a new threading approach, CEthreader, which allows for dynamic programing alignments of predicted contact-maps through eigen-decomposition. The large-scale benchmark tests show that the coupling of contact map with profile and secondary structure alignments through the proposed protocol can significantly improve the accuracy of template recognition for distantly-homologous protein targets.

A large-scale comparative assessment of methods for residue-residue contact prediction

Sequence-based prediction of residue-residue contact in proteins becomes increasingly more important for improving protein structure prediction in the big data era. In this study, we performed a large-scale comparative assessment of 15 locally installed contact predictors. To assess these methods, we collected a big data set consisting of 680 nonredundant proteins covering different structural classes and target difficulties. We investigated a wide range of factors that may influence the precision of contact prediction, including target difficulty, structural class, the alignment depth and distribution of contact pairs in a protein structure. We found that: (1) the machine learning-based methods outperform the direct-coupling-based methods for short-range contact prediction, while the latter are significantly better for long-range contact prediction. The consensus-based methods, which combine machine learning and direct-coupling methods, perform the best. (2) The target difficulty does not have clear influence on the machine learning-based methods, while it does affect the direct-coupling and consensus-based methods significantly. (3) The alignment depth has relatively weak effect on the machine learning-based methods. However, for the direct-coupling-based methods and consensus-based methods, the predicted contacts for targets with deeper alignment tend to be more accurate. (4) All methods perform relatively better on β and α + β proteins than on α proteins. (5) Residues buried in the core of protein structure are more prone to be in contact than residues on the surface (22 versus 6%). We believe these are useful results for guiding future development of new approach to contact prediction.

Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set

Lysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Therefore, the alternative computational methods are necessary. Here, we developed a novel tool, KA-predictor, to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. We incorporated different types of features and employed an efficient feature selection on each type to form the final optimal feature set for model learning. And our predictor was highly competitive for the majority of species when compared with other methods. Feature contribution analysis indicated that HSE features, which were firstly introduced for lysine acetylation prediction, significantly improved the predictive performance. Particularly, we constructed a high-accurate structure dataset of H.sapiens from PDB to analyze the structural properties around lysine acetylation sites. Our datasets and a user-friendly local tool of KA-predictor can be freely available at http://sourceforge.net/p/ka-predictor.

[Quantitative determination of methyl-sulf amino-acid by internal standard-derivative infrared spectroscopy]

With pressed kBr disk technique the methyl-sulf amino-acid--the nutritious restrictive amino-acid of poultry was determined by internal standard-derivative infrared spectroscopy. The suitable internal standard--CaSO4 was selected, and peak height, peak area, first derivative and second derivative methods were used. In the determination, we selected undisturbed peak at 553 cm-1 as standard. The correlation coefficient of standard curve equation were in the range of 0.994-0.998. The result was satisfied.