Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.

Macrophages promote cartilage regeneration in a time- and phenotype-dependent manner.

Immune regulation of osteochondral defect regeneration has not yet been rigorously characterized. Although macrophages have been demonstrated to regulate the regeneration process in various tissues, their direct contribution to cartilage regeneration remains to be investigated, particularly the functions of polarized macrophage subpopulations. In this study, we investigated the origins and functions of macrophages during healing of osteochondral injury in the murine model. Upon osteochondral injury, joint macrophages are predominantly derived from circulating monocytes. Macrophages are essential for spontaneous cartilage regeneration in juvenile C57BL/6 mice, by modulating proliferation and apoptosis around the injury site. Exogeneous macrophages also exhibit therapeutic potential in promoting cartilage regeneration in adult mice with poor regenerative capacity, possibly via regulation of PDGFRα+  stem cells, with this process being influenced by initial phenotype and administration timing. Only M2c macrophages are able to promote regeneration of both cartilage tissues and subchondral bone. Overall, we reveal the direct link between macrophages and osteochondral regeneration and highlight the key roles of relevant immunological niches in successful regeneration.

Personalized Research on Diet in Ulcerative Colitis and Crohn's Disease: A Series of N-of-1 Diet Trials.

INTRODUCTION: Evidence about specific carbohydrate diet (SCD) for inflammatory bowel disease (IBD) is limited. We conducted 54 single-subject, double-crossover N-of-1 trials comparing SCD with a modified SCD (MSCD) and comparing each with the participant's baseline, usual diet (UD). METHODS: Across 19 sites, we recruited patients aged 7-18 years with IBD and active inflammation. Following a 2-week baseline (UD), patients were randomized to 1 of 2 sequences of 4 alternating 8-week SCD and MSCD periods. Outcomes included fecal calprotectin and patient-reported symptoms. We report posterior probabilities from Bayesian models comparing diets. RESULTS: Twenty-one (39%) participants completed the trial, 9 (17%) completed a single crossover, and 24 (44%) withdrew. Withdrawal or early completion occurred commonly (lack of response [n = 11], adverse events [n = 11], and not desiring to continue [n = 6]). SCD and MSCD performed similarly for most individuals. On average, there was <1% probability of a clinically meaningful difference in IBD symptoms between SCD and MSCD. The average treatment difference was -0.3 (95% credible interval -1.2, 0.75). There was no significant difference in the ratio of fecal calprotectin geometric means comparing SCD and MSCD (0.77, 95% credible interval 0.51, 1.10). Some individuals had improvement in symptoms and fecal calprotectin compared with their UD, whereas others did not. DISCUSSION: SCD and MSCD did not consistently improve symptoms or inflammation, although some individuals may have benefited. However, there are inherent difficulties in examining dietary changes that complicate study design and ultimately conclusions regarding effectiveness.

Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data.

We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT algorithms are extensions of the classification and regression tree algorithm that use splitting criteria based on subgroup-specific treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g-formula, and doubly robust estimators of subgroup-specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients.

Assessing additive effects of air pollutants on mortality rate in Massachusetts.

BACKGROUND: We previously found additive effects of long- and short-term exposures to fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) on all-cause mortality rate using a generalized propensity score (GPS) adjustment approach. The study addressed an important question of how many early deaths were caused by each exposure. However, the study was computationally expensive, did not capture possible interactions and high-order nonlinearities, and omitted potential confounders. METHODS: We proposed two new methods and reconducted the analysis using the same cohort of Medicare beneficiaries in Massachusetts during 2000-2012, which consisted of 1.5 million individuals with 3.8 billion person-days of follow-up. The first method, weighted least squares (WLS), leveraged large volume of data by aggregating person-days, which gave equivalent results to the linear probability model (LPM) method in the previous analysis but significantly reduced computational burden. The second method, m-out-of-n random forests (moonRF), implemented scaling random forests that captured all possible interactions and nonlinearities in the GPS model. To minimize confounding bias, we additionally controlled relative humidity and health care utilizations that were not included previously. Further, we performed low-level analysis by restricting to person-days with exposure levels below increasingly stringent thresholds. RESULTS: We found consistent results between LPM/WLS and moonRF: all exposures were positively associated with mortality rate, even at low levels. For long-term PM2.5 and O3, the effect estimates became larger at lower levels. Long-term exposure to PM2.5 posed the highest risk: 1 µg/m3 increase in long-term PM2.5 was associated with 1053 (95% confidence interval [CI]: 984, 1122; based on LPM/WLS methods) or 1058 (95% CI: 988, 1127; based on moonRF method) early deaths each year among the Medicare population in Massachusetts. CONCLUSIONS: This study provides more rigorous causal evidence between PM2.5, O3, and NO2 exposures and mortality, even at low levels. The largest effect estimate for long-term PM2.5 suggests that reducing PM2.5 could gain the most substantial benefits. The consistency between LPM/WLS and moonRF suggests that there were not many interactions and high-order nonlinearities. In the big data context, the proposed methods will be useful for future scientific work in estimating causality on an additive scale.

Subgroup identification using covariate-adjusted interaction trees.

We consider the problem of identifying subgroups of participants in a clinical trial that have enhanced treatment effect. Recursive partitioning methods that recursively partition the covariate space based on some measure of between groups treatment effect difference are popular for such subgroup identification. The most commonly used recursive partitioning method, the classification and regression tree algorithm, first creates a large tree by recursively partitioning the covariate space using some splitting criteria and then selects the final tree from all the subtrees of the large tree. In the context of subgroup identification, calculation of the splitting criteria and the evaluation measure used for final tree selection rely on comparing differences in means between the treatment and control arm. When covariates are prognostic for the outcome, covariate adjusted estimators have the ability to improve efficiency compared to using differences in averages between the treatment and control group. This manuscript develops two covariate adjusted estimators that can be used to both make splitting decisions and for final tree selection. The performance of the resulting covariate adjusted recursive partitioning algorithm is evaluated using simulations and by analyzing a clinical trial that evaluates if motivational interviews improve treatment engagement for substance abusers.

A Clinical Decision Support Tool to Predict Cancer Risk for Commonly Tested Cancer-Related Germline Mutations.

The rapid drop in the cost of DNA sequencing led to the availability of multi-gene panels, which test 25 or more cancer susceptibility genes for a low cost. Clinicians and genetic counselors need a tool to interpret results, understand risk of various cancers, and advise on a management strategy. This is challenging as there are multiple studies regarding each gene, and it is not possible for clinicians and genetic counselors to be aware of all publications, nor to appreciate the relative accuracy and importance of each. Through an extensive literature review, we have identified reliable studies and derived estimates of absolute risk. We have also developed a systematic mechanism and informatics tools for (1) data curation, (2) the evaluation of quality of studies, and (3) the statistical analysis necessary to obtain risk. We produced the risk prediction clinical decision support tool ASK2ME (All Syndromes Known to Man Evaluator). It provides absolute cancer risk predictions for various hereditary cancer susceptibility genes. These predictions are specific to patients' gene carrier status, age, and history of relevant prophylactic surgery. By allowing clinicians to enter patient information and receive patient-specific cancer risks, this tool aims to have a significant impact on the quality of precision cancer prevention and disease management activities relying on panel testing. It is important to note that this tool is dynamic and constantly being updated, and currently, some of its limitations include (1) for many gene-cancer associations risk estimates are based on one study rather than meta-analysis, (2) strong assumptions on prior cancers, (3) lack of uncertainty measures, and (4) risk estimates for a growing set of gene-cancer associations which are not always variant specific. All of these concerns are being addressed on an ongoing basis, aiming to make the tool even more accurate.

Bayesian Models for N-of-1 Trials.

We describe Bayesian models for data from N-of-1 trials, reviewing both the basics of Bayesian inference and applications to data from single trials and collections of trials sharing the same research questions and data structures. Bayesian inference is natural for drawing inferences from N-of-1 trials because it can incorporate external and subjective information to supplement trial data as well as give straightforward interpretations of posterior probabilities as an individual's state of knowledge about their own condition after their trial. Bayesian models are also easily augmented to incorporate specific characteristics of N-of-1 data such as trend, carryover, and autocorrelation and offer flexibility of implementation. Combining data from multiple N-of-1 trials using Bayesian multilevel models leads naturally to inferences about population and subgroup parameters such as average treatment effects and treatment effect heterogeneity and to improved inferences about individual parameters. Data from a trial comparing different diets for treating children with inflammatory bowel disease are used to illustrate the models and inferences that may be drawn. The analysis shows that certain diets were better on average at reducing pain, but that benefits were restricted to a subset of patients and that withdrawal from the study was a good marker for lack of benefit.

The Impact of Exposure Measurement Error on the Estimated Concentration-Response Relationship between Long-Term Exposure to PM2.5 and Mortality.

BACKGROUND: Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed. OBJECTIVES: We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)] and all-cause mortality. METHODS: In this simulation study, we use daily PM2.5 predictions at 1-km2 spatial resolution to estimate annual PM2.5 exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration-response relationship between PM2.5 exposure and mortality (linear, quadratic, or soft-threshold-i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration-response relationship between PM2.5 exposure and mortality, adjusting for the confounders. RESULTS: We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels. DISCUSSION: Our findings suggest that the causal determination for long-term PM2.5 exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389.

Individualized Studies of Triggers of Paroxysmal Atrial Fibrillation: The I-STOP-AFib Randomized Clinical Trial.

Importance: Atrial fibrillation (AF) is the most common arrhythmia. Although patients have reported that various exposures determine when and if an AF event will occur, a prospective evaluation of patient-selected triggers has not been conducted, and the utility of characterizing presumed AF-related triggers for individual patients remains unknown. Objective: To test the hypothesis that n-of-1 trials of self-selected AF triggers would enhance AF-related quality of life. Design, Setting, and Participants: A randomized clinical trial lasting a minimum of 10 weeks tested a smartphone mobile application used by symptomatic patients with paroxysmal AF who owned a smartphone and were interested in testing a presumed AF trigger. Participants were screened between December 22, 2018, and March 29, 2020. Interventions: n-of-1 Participants received instructions to expose or avoid self-selected triggers in random 1-week blocks for 6 weeks, and the probability their trigger influenced AF risk was then communicated. Controls monitored their AF over the same time period. Main Outcomes and Measures: AF was assessed daily by self-report and using a smartphone-based electrocardiogram recording device. The primary outcome comparing n-of-1 and control groups was the Atrial Fibrillation Effect on Quality-of-Life (AFEQT) score at 10 weeks. All participants could subsequently opt for additional trigger testing. Results: Of 446 participants who initiated (mean [SD] age, 58 [14] years; 289 men [58%]; 461 White [92%]), 320 (72%) completed all study activities. Self-selected triggers included caffeine (n = 53), alcohol (n = 43), reduced sleep (n = 31), exercise (n = 30), lying on left side (n = 17), dehydration (n = 10), large meals (n = 7), cold food or drink (n = 5), specific diets (n = 6), and other customized triggers (n = 4). No significant differences in AFEQT scores were observed between the n-of-1 vs AF monitoring-only groups. In the 4-week postintervention follow-up period, significantly fewer daily AF episodes were reported after trigger testing compared with controls over the same time period (adjusted relative risk, 0.60; 95% CI, 0.43- 0.83; P < .001). In a meta-analysis of the individualized trials, only exposure to alcohol was associated with significantly heightened risks of AF events. Conclusions and Relevance: n-of-1 Testing of AF triggers did not improve AF-associated quality of life but was associated with a reduction in AF events. Acute exposure to alcohol increased AF risk, with no evidence that other exposures, including caffeine, more commonly triggered AF. Trial Registration: ClinicalTrials.gov Identifier: NCT03323099.

Modified hyaluronic acid hydrogels with chemical groups that facilitate adhesion to host tissues enhance cartilage regeneration.

Stable integration of hydrogel implants with host tissues is of critical importance to cartilage tissue engineering. Designing and fabricating hydrogels with high adhesive strength, stability and regeneration potential are major challenges to be overcome. This study fabricated injectable adhesive hyaluronic acid (HA) hydrogel modified by aldehyde groups and methacrylate (AHAMA) on the polysaccharide backbone with multiple anchoring mechanisms (amide bond through the dynamic Schiff base reaction, hydrogen bond and physical interpenetration). AHAMA hydrogel exhibited significantly improved durability and stability within a humid environment (at least 7 days), together with higher adhesive strength (43 KPa to skin and 52 KPa to glass), as compared to commercial fibrin glue (nearly 10 KPa) and HAMA hydrogel (nearly 20 KPa). The results showed that AHAMA hydrogel was biocompatible and could be easily and rapidly prepared in situ. In vitro cell culture experiments showed that AHAMA hydrogel could enhance proliferation (1.2-folds after 3 days) and migration (1.5-folds after 12 h) of bone marrow stem cells (BMSCs), as compared to cells cultured in a culture dish. Furthermore, in a rat osteochondral defect model, implanted AHAMA hydrogel significantly promoted integration between neo-cartilage and host tissues, and significantly improved cartilage regeneration (modified O'Driscoll histological scores of 16.0 ± 4.1 and 18.3 ± 4.6 after 4 and 12-weeks of post-implantation in AHAMA groups respectively, 12.0 ± 2.7 and 12.2 ± 2.8 respectively in HAMA groups, 9.8 ± 2.4 and 11.5 ± 2.1 respectively in untreated groups). Hence, AHAMA hydrogel is a promising adhesive biomaterial for clinical cartilage regeneration and other biomedical applications.

Multiple sites on SARS-CoV-2 spike protein are susceptible to proteolysis by cathepsins B, K, L, S, and V.

SARS-CoV-2 is the coronavirus responsible for the COVID-19 pandemic. Proteases are central to the infection process of SARS-CoV-2. Cleavage of the spike protein on the virus's capsid causes the conformational change that leads to membrane fusion and viral entry into the target cell. Since inhibition of one protease, even the dominant protease like TMPRSS2, may not be sufficient to block SARS-CoV-2 entry into cells, other proteases that may play an activating role and hydrolyze the spike protein must be identified. We identified amino acid sequences in all regions of spike protein, including the S1/S2 region critical for activation and viral entry, that are susceptible to cleavage by furin and cathepsins B, K, L, S, and V using PACMANS, a computational platform that identifies and ranks preferred sites of proteolytic cleavage on substrates, and verified with molecular docking analysis and immunoblotting to determine if binding of these proteases can occur on the spike protein that were identified as possible cleavage sites. Together, this study highlights cathepsins B, K, L, S, and V for consideration in SARS-CoV-2 infection and presents methodologies by which other proteases can be screened to determine a role in viral entry. This highlights additional proteases to be considered in COVID-19 studies, particularly regarding exacerbated damage in inflammatory preconditions where these proteases are generally upregulated.

Chronic pain treatment preferences change following participation in N-of-1 trials, but not always in the expected direction.

OBJECTIVE: To examine pain treatment preferences before and after participation in an N-of-1 trial. STUDY DESIGN AND SETTING: In this observational study nested within a randomized trial, we examined chronic pain patients' preferences before and after treatment in relation to N-of-1 trial results; assessed the influence of different schemes for defining comparative "superiority" on potential conclusions; and generated classification trees illustrating the relationship between pre-treatment preferences, N-of-1 trial results, and post-treatment preferences. RESULTS: Treatment preferences differed pre- and post-trial for 40% of participants. The proportion of patients whose N-of-1 trials demonstrated "superiority" of one treatment regimen over the other varied depending on how superiority was defined and ranged from 24% (using criteria that required statistically significant differences between regimens) to 62% (when relying only on differences in point estimates). Regardless of criteria for declaring treatment superiority, nearly three-fourths of patients with equivocal N-of-1 trial results nevertheless expressed definite preferences post-trial. CONCLUSION: A large segment of patients undergoing N-of-1 trials for chronic pain altered their treatment preferences. However, the direction of preference change did not necessarily correspond to the N-of-1 results. More research is needed to understand how patients use N-of-1 trial results, why preferences are "sticky" even in the face of personalized data, and how patients and clinicians might be educated to use N-of-1 trial results more informatively.

Multiple nanosecond pulsed electric fields stimulation with conductive poly(l-lactic acid)/carbon nanotubes films maintains the multipotency of mesenchymal stem cells during prolonged in vitro culture.

Mesenchymal stem cells (MSCs) gradually lose multipotency when cultured for prolonged durations in vitro, which significantly hinders subsequent clinical applications. Nanosecond pulsed electric fields (nsPEFs) have been recently investigated to overcome this problem in our lab; however, the differentiation potency of MSCs could only be partially and transiently recovered because the nsPEFs can only be delivered to suspended cells once. Here, we develop a new strategy to apply multiple nsPEFs to adherent MSCs with conductive films to mitigate the decreasing multipotency of prolonged cultured MSCs. The poly(l-lactic acid)/graphitized-carboxylated functionalized carbon nanotubes (PLLA/CNT) films were fabricated as conductive cell culture platforms. Both single and multiple nsPEFs stimulation could significantly increase the differentiation potential of MSCs, as evidenced by upregulated expression of chondrogenic, osteogenic, and adipogenic-related gene (SOX9, RUNX2, and PPAR-γ), as well as increased production of proteoglycans, mineralized calcium, and triglycerides. Multiple nsPEFs stimulation demonstrated significant efficacy in upregulating expression of pluripotency genes of OCT4A (3.5- to 4.5-folds), NANOG (3.5- to 4.0-folds), and SOX2 (1.5- to 2.0-folds) and stably maintaining high expression of these genes for nearly 23 days. Notably, nsPEFs stimulation did not significantly shorten telomere length. In conclusion, multiple nsPEFs stimulation could effectively mitigate decreasing multipotency of MSCs during prolonged in vitro culture.

Feasibility, Acceptability, and Influence of mHealth-Supported N-of-1 Trials for Enhanced Cognitive and Emotional Well-Being in US Volunteers.

Although group-level evidence supports the use of behavioral interventions to enhance cognitive and emotional well-being, different interventions may be more acceptable or effective for different people. N-of-1 trials are single-patient crossover trials designed to estimate treatment effectiveness in a single patient. We designed a mobile health (mHealth) supported N-of-1 trial platform permitting US adult volunteers to conduct their own 30-day self-experiments testing a behavioral intervention of their choice (deep breathing/meditation, gratitude journaling, physical activity, or helpful acts) on daily measurements of stress, focus, and happiness. We assessed uptake of the study, perceived usability of the N-of-1 trial system, and influence of results (both reported and perceived) on enthusiasm for the chosen intervention (defined as perceived helpfulness of the chosen intervention and intent to continue performing the intervention in the future). Following a social media and public radio campaign, 447 adults enrolled in the study and 259 completed the post-study survey. Most were highly educated. Perceived system usability was high (mean scale score 4.35/5.0, SD 0.57). Enthusiasm for the chosen intervention was greater among those with higher pre-study expectations that the activity would be beneficial for them (p < 0.001), those who obtained more positive N-of-1 results (as directly reported to participants) (p < 0.001), and those who interpreted their N-of-1 study results more positively (p < 0.001). However, reported results did not significantly influence enthusiasm after controlling for participants' interpretations. The interaction between pre-study expectation of benefit and N-of-1 results interpretation was significant (p < 0.001), such that those with the lowest starting pre-study expectations reported greater intervention enthusiasm when provided with results they interpreted as positive. We conclude that N-of-1 behavioral trials can be appealing to a broad albeit highly educated and mostly female audience, that usability was acceptable, and that N-of-1 behavioral trials may have the greatest utility among those most skeptical of the intervention to begin with.

Associations between seasonal temperature and dementia-associated hospitalizations in New England.

Human-induced climate change has accelerated in recent decades, causing adverse health effects. However, the impact of the changing climate on neurological disorders in the older population is not well understood. We applied time-varying Cox proportional hazards models to estimate the associations between hospital admissions for dementia and the mean and variability of summer and winter temperatures in New England. We estimated seasonal temperatures for each New England zip code using a satellite-based prediction model. By characterizing spatial differences and temporal fluctuations in seasonal temperatures, we observed a lower risk of dementia-associated hospital admissions in years when local temperatures in either summer (hazard ration [HR] = 0.98; 95% confidence interval [CI]: 0.96, 1.00) or winter (HR = 0.97; 95% CI: 0.94, 0.99) were higher than average, and a greater risk of dementia-associated admissions for older adults living in zip codes with higher temperature variations. Effect modifications by sex, race, age, and dual eligibility were considered to examine vulnerability of population subgroups. Our results suggest that cooler-than-average temperatures and higher temperature variability increase the risk of dementia-associated hospital admissions. Thus, climate change may affect progression of dementia and associated hospitalization costs.

TGF-ß1 affinity peptides incorporated within a chitosan sponge scaffold can significantly enhance cartilage regeneration.

Growth factors, such as TGF-ß and BMPs, play key roles in the chondrogenic differentiation of mesenchymal stem cells (MSCs) and cartilage regeneration in vivo. Nevertheless, there are some technical challenges in delivering exogenous growth factors in vivo, such as burst release and loss of bioactivity. In this study, TGF-ß1 affinity peptides were incorporated within porous chitosan scaffolds to enhance cartilage regeneration. Significant upregulation of gene expression levels of Sox9, Col II and AGG during chondrogenic differentiation of MSCs in vitro, were positively correlated with increasing amounts of TGF-ß1 affinity peptides incorporated within the chitosan scaffolds. The results of ectopic implantation of scaffolds in nude mice showed that incorporation of TGF-ß1 affinity peptides and preloading of TGF-ß1 synergistically enhanced ectopic cartilage formation at both high and low cell densities. Furthermore, in a rabbit osteochondral defect model, implantation of chitosan scaffolds incorporated with TGF-ß1 affinity peptides (CHI-PEP) could significantly promote cartilage regeneration, even in the absence of exogenous growth factors and seeded cells. Notably, inflammation and cartilage degeneration were markedly alleviated in the CHI-PEP group. Hence, incorporation of TGF-ß1 affinity peptide within the chitosan sponge scaffold significantly enhanced articular cartilage regeneration.

SysPTM 2.0: an updated systematic resource for post-translational modification.

Post-translational modifications (PTMs) of proteins play essential roles in almost all cellular processes, and are closely related to physiological activity and disease development of living organisms. The development of tandem mass spectrometry (MS/MS) has resulted in a rapid increase of PTMs identified on proteins from different species. The collection and systematic ordering of PTM data should provide invaluable information for understanding cellular processes and signaling pathways regulated by PTMs. For this original purpose we developed SysPTM, a systematic resource installed with comprehensive PTM data and a suite of web tools for annotation of PTMs in 2009. Four years later, there has been a significant advance with the generation of PTM data and, consequently, more sophisticated analysis requirements have to be met. Here we submit an updated version of SysPTM 2.0 (http://lifecenter.sgst.cn/SysPTM/), with almost doubled data content, enhanced web-based analysis tools of PTMBlast, PTMPathway, PTMPhylog, PTMCluster. Moreover, a new session SysPTM-H is constructed to graphically represent the combinatorial histone PTMs and dynamic regulation of histone modifying enzymes, and a new tool PTMGO is added for functional annotation and enrichment analysis. SysPTM 2.0 not only facilitates resourceful annotation of PTM sites but allows systematic investigation of PTM functions by the user. Database URL: http://lifecenter.sgst.cn/SysPTM/.