Hypoxic indices for obstructive sleep apnoea severity and cardiovascular disease risk prediction: A comparison and application in a community population.

BACKGROUND AND OBJECTIVE: The apnoea-hypopnoea index (AHI) and oxygen desaturation index (ODI) encounter challenges in capturing the intricate relationship between obstructive sleep apnoea (OSA) and cardiovascular disease (CVD) risks. Although novel hypoxic indices have been proposed to tackle these limitations, there remains a gap in comprehensive validation and comparisons across a unified dataset. METHODS: Samples were derived from the Sleep Heart Health Study (SHHS), involving 4485 participants aged over 40 years after data quality screening. The study compared several key indices, including AHI, ODI, the reconstructed hypoxic burden (rHB), the percentage of sleep time with the duration of respiratory events causing desaturation (pRED_3p) and the sleep breathing impairment index (SBII), in relation to CVD mortality and morbidity risks. Adjusted Cox proportional models were employed to calculate hazard ratios (HRs) for each index, and comparisons were performed. RESULTS: SBII and pRED_3p exhibited significant correlations with both CVD mortality and morbidity, with SBII showing the highest adjusted HR (95% confidence interval) for mortality (2.04 [1.25, 3.34]) and pRED_3p for morbidity (1.43 [1.09-1.88]). In contrast, rHB was only significant in predicting CVD mortality (1.63 [1.05-2.53]), while AHI and ODI did not show significant correlations with CVD outcomes. The adjusted models based on SBII and pRED_3p exhibited optimal performance in the CVD mortality and morbidity datasets, respectively. CONCLUSION: This study identified the optimal indices for OSA-related CVD risks prediction, SBII for mortality and pRED_3p for morbidity. The open-source online platform provides the computation of the indices.

The potential interaction between chemosensitivity and the development of cardiovascular disease in obstructive sleep apnea.

OBJECTIVES: Chemosensitivity is an essential part of the pathophysiological mechanisms of obstructive sleep apnea (OSA). Not only does OSA have a certain relationship with the comorbidity of cardiovascular disease (CVD) but also chemosensitivity plays a crucial role in the development of CVD. This study aims to investigate the potential interaction between chemosensitivity and the development of CVD in OSA. METHODS: A total of 169 participants with suspected OSA were included. Data were gathered on the parameters of polysomnography and baseline clinical features. Peripheral chemosensitivity was evaluated by employing the rebreathing test. The lifetime CVD risk was computed using the China-PAR (Prediction for atherosclerotic CVD Risk in China) risk equation. RESULTS: After controlling for covariates, participants with chemosensitivity levels in the second and fifth quantiles tended to hold an increased proportion of high lifetime CVD risk (OR 10.90, 95%CI [2.81-42.28]; OR 6.78, 95%CI [1.70-27.05], respectively). The diagnosis of OSA would significantly increase the 10-year and lifetime CVD risks in participants with low chemosensitivity, while no such differences were found in participants with high chemosensitivity. CONCLUSION: Higher lifetime CVD risk was associated with participants who had greater peripheral chemosensitivity. In terms of the CVD outcomes, adult patients with a relatively low level of chemosensitivity may be primarily related to their diagnosis of OSA, whereas adult patients with a relatively high level of chemosensitivity may be more strongly associated with their elevated levels of chemosensitivity rather than OSA.

Short-term effect of particulate matter on lung function and impulse oscillometry system (IOS) parameters of chronic obstructive pulmonary disease (COPD) in Beijing, China.

OBJECTIVE: This study aimed to evaluate the associations between particulate matter (PM), lung function and Impulse Oscillometry System (IOS) parameters in chronic obstructive pulmonary disease (COPD) patients and identity effects between different regions in Beijing, China. METHODS: In this retrospective study, we recruited 1348 outpatients who visited hospitals between January 2016 and December 2019. Ambient air pollutant data were obtained from the central monitoring stations nearest the participants' residential addresses. We analyzed the effect of particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) exposure on lung function and IOS parameters using a multiple linear regression model, adjusting for sex, smoking history, education level, age, body mass index (BMI), mean temperature, and relative humidity . RESULTS: The results showed a relationship between PM2.5, lung function and IOS parameters. An increase of 10 µg/m3 in PM2.5 was associated with a decline of 2.083% (95% CI: -3.047 to - 1.103) in forced expiratory volume in one second /predict (FEV1%pred), a decline of 193 ml/s (95% CI: -258 to - 43) in peak expiratory flow (PEF), a decline of 0.932% (95% CI: -1.518 to - 0.342) in maximal mid-expiratory flow (MMEF); an increase of 0.732 Hz (95% CI: 0.313 to 1.148) in resonant frequency (Fres), an increase of 36 kpa/(ml/s) (95% CI: 14 to 57) in impedance at 5 Hz (Z5) and an increase of 31 kpa/(ml/s) (95% CI: 2 to 54) in respiratory impedance at 5 Hz (R5). Compared to patients in the central district, those in the southern district had lower FEV1/FVC, FEV1%pred, PEF, FEF75%, MMEF, X5, and higher Fres, Z5 and R5 (p < 0.05). CONCLUSION: Short-term exposure to PM2.5 was associated with reductions in lung function indices and an increase in IOS results in patients with COPD. The heavier the PM2.5, the more severe of COPD.

The Association Between Cognition of Obstructive Sleep Apnea Patients and Urinary AD7c-NTP Level: Investigation and Application.

BACKGROUND: Obstructive sleep apnea (OSA) is a multi-component disorder, which has many comorbidities, including cognitive impairment. Although its potential risk factors were unknown, they could affect the patient's quality of life and long-term prognosis. OBJECTIVE: The purpose of this study was to investigate the application of urinary Alzheimer's disease-associated neurofilament protein (AD7c-NTP) levels in the assessment of cognitive impairment in OSA patients, and to analyze the predictive value of potential high-risk factors on cognitive impairment in OSA patients. METHODS: 138 young and middle-aged adults were recruited and underwent overnight polysomnographic recording, Montreal Cognitive Assessment (MoCA), and urinary AD7c-NTP test. AD7c-NTP and other factors were further applied as biomarkers to develop a cognition risk prediction model. RESULTS: Compared with the control, OSA patients showed significantly lower MoCA scores and higher urinary AD7c-NTP concentrations, while the severe OSA group appeared more significant. The urinary AD7c-NTP level of the OSA cognitive impairment group was higher than that of the non-cognitive impairment group. The results of regression analysis showed that urinary AD7c-NTP level was an independent predictor of cognitive impairment in OSA patients. Based on urinary AD7c-NTP levels and other selected factors, a multimodal prediction model for assessing the risk of cognitive impairment in OSA patients was initially established. CONCLUSION: The increased urinary AD7c-NTP level could be used as a relevant peripheral biomarker of cognitive impairment in OSA patients. A model using urinary AD7c-NTP combined with other factors was developed and could accurately assess the cognition risk of OSA patients.

Identification of Human Global, Tissue and Within-Tissue Cell-Specific Stably Expressed Genes at Single-Cell Resolution.

Stably Expressed Genes (SEGs) are a set of genes with invariant expression. Identification of SEGs, especially among both healthy and diseased tissues, is of clinical relevance to enable more accurate data integration, gene expression comparison and biomarker detection. However, it remains unclear how many global SEGs there are, whether there are development-, tissue- or cell-specific SEGs, and whether diseases can influence their expression. In this research, we systematically investigate human SEGs at single-cell level and observe their development-, tissue- and cell-specificity, and expression stability under various diseased states. A hierarchical strategy is proposed to identify a list of 408 spatial-temporal SEGs. Development-specific SEGs are also identified, with adult tissue-specific SEGs enriched with the function of immune processes and fetal tissue-specific SEGs enriched in RNA splicing activities. Cells of the same type within different tissues tend to show similar SEG composition profiles. Diseases or stresses do not show influence on the expression stableness of SEGs in various tissues. In addition to serving as markers and internal references for data normalization and integration, we examine another possible application of SEGs, i.e., being applied for cell decomposition. The deconvolution model could accurately predict the fractions of major immune cells in multiple independent testing datasets of peripheral blood samples. The study provides a reliable list of human SEGs at the single-cell level, facilitates the understanding on the property of SEGs, and extends their possible applications.

Computational prediction of secreted proteins in gram-negative bacteria.

Gram-negative bacteria harness multiple protein secretion systems and secrete a large proportion of the proteome. Proteins can be exported to periplasmic space, integrated into membrane, transported into extracellular milieu, or translocated into cytoplasm of contacting cells. It is important for accurate, genome-wide annotation of the secreted proteins and their secretion pathways. In this review, we systematically classified the secreted proteins according to the types of secretion systems in Gram-negative bacteria, summarized the known features of these proteins, and reviewed the algorithms and tools for their prediction.

T1SEstacker: A Tri-Layer Stacking Model Effectively Predicts Bacterial Type 1 Secreted Proteins Based on C-Terminal Non-repeats-in-Toxin-Motif Sequence Features.

Type 1 secretion systems play important roles in pathogenicity of Gram-negative bacteria. However, the substrate secretion mechanism remains largely unknown. In this research, we observed the sequence features of repeats-in-toxin (RTX) proteins, a major class of type 1 secreted effectors (T1SEs). We found striking non-RTX-motif amino acid composition patterns at the C termini, most typically exemplified by the enriched "[FLI][VAI]" at the most C-terminal two positions. Machine-learning models, including deep-learning ones, were trained using these sequence-based non-RTX-motif features and further combined into a tri-layer stacking model, T1SEstacker, which predicted the RTX proteins accurately, with a fivefold cross-validated sensitivity of ∼0.89 at the specificity of ∼0.94. Besides substrates with RTX motifs, T1SEstacker can also well distinguish non-RTX-motif T1SEs, further suggesting their potential existence of common secretion signals. T1SEstacker was applied to predict T1SEs from the genomes of representative Salmonella strains, and we found that both the number and composition of T1SEs varied among strains. The number of T1SEs is estimated to reach 100 or more in each strain, much larger than what we expected. In summary, we made comprehensive sequence analysis on the type 1 secreted RTX proteins, identified common sequence-based features at the C termini, and developed a stacking model that can predict type 1 secreted proteins accurately.

A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features.

BACKGROUND: Stomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations. METHODS: In this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association. RESULTS: Typical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly. CONCLUSION: The study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.

Improving the diversity of captured full-length isoforms using a normalized single-molecule RNA-sequencing method.

Human genes form a large variety of isoforms after transcription, encoding distinct transcripts to exert different functions. Single-molecule RNA sequencing facilitates accurate identification of the isoforms by extending nucleotide read length significantly. However, the gene or isoform diversity is lowly represented by the mRNA molecules captured by single-molecule RNA sequencing. Here, we show that a cDNA normalization procedure before the library preparation for PacBio RS II sequencing captures 3.2-6.0 fold more full-length high-quality isoform species for different human samples, as compared to the non-normalized capture procedure. Many lowly expressed, functionally important isoforms can be detected. In addition, normalized PacBio RNA sequencing also resolves more allele-specific haplotype transcripts. Finally, we apply the cDNA normalization based long-read RNA sequencing method to profile the transcriptome of human gastric signet-ring cell carcinomas, identify new cancer-specific transcriptome signatures, and thus, bring out the utility of the improved protocols in gene expression studies.

T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors.

Many Gram-negative bacteria infect hosts and cause diseases by translocating a variety of type III secreted effectors (T3SEs) into the host cell cytoplasm. However, despite a dramatic increase in the number of available whole-genome sequences, it remains challenging for accurate prediction of T3SEs. Traditional prediction models have focused on atypical sequence features buried in the N-terminal peptides of T3SEs, but unfortunately, these models have had high false-positive rates. In this research, we integrated promoter information along with characteristic protein features for signal regions, chaperone-binding domains, and effector domains for T3SE prediction. Machine learning algorithms, including deep learning, were adopted to predict the atypical features mainly buried in signal sequences of T3SEs, followed by development of a voting-based ensemble model integrating the individual prediction results. We assembled this into a unified T3SE prediction pipeline, T3SEpp, which integrated the results of individual modules, resulting in high accuracy (i.e., ∼0.94) and >1-fold reduction in the false-positive rate compared to that of state-of-the-art software tools. The T3SEpp pipeline and sequence features observed here will facilitate the accurate identification of new T3SEs, with numerous benefits for future studies on host-pathogen interactions.IMPORTANCE Type III secreted effector (T3SE) prediction remains a big computational challenge. In practical applications, current software tools often suffer problems of high false-positive rates. One of the causal factors could be the relatively unitary type of biological features used for the design and training of the models. In this research, we made a comprehensive survey on the sequence-based features of T3SEs, including signal sequences, chaperone-binding domains, effector domains, and transcription factor binding promoter sites, and assembled a unified prediction pipeline integrating multi-aspect biological features within homology-based and multiple machine learning models. To our knowledge, we have compiled the most comprehensive biological sequence feature analysis for T3SEs in this research. The T3SEpp pipeline integrating the variety of features and assembling different models showed high accuracy, which should facilitate more accurate identification of T3SEs in new and existing bacterial whole-genome sequences.

Combination of Genetic Markers and Age Effectively Facilitates the Identification of People with High Risk of Preeclampsia in the Han Chinese Population.

OBJECTIVE: This study aimed to analyze the possible association between known genetic risks and preeclampsia in a Han Chinese population. METHODS: A total of 156 patients with preeclampsia and 286 healthy Han Chinese women were enrolled and genotyped for 27 genetic alleles associated with preeclampsia in different populations. The association between the genotypes of the individual alleles and preeclampsia and the possible interaction among the alleles were analyzed. Finally logistic models were trained with the genotypes of possible alleles contributing to preeclampsia. RESULTS: Seven alleles were significantly or marginally significantly associated with preeclampsia, which involved six genes (rs4762 in AGT, rs1800896 in IL-10, rs1800629 and rs1799724 in TNFα, rs2070744 in NOS3, rs7412 in APOE, and rs2549782 in ERAP2). A multilocus interaction analysis further disclosed an interaction among seven alleles. A logistic model showing individual or synergetic contribution to preeclampsia could reach ~0.67 preeclampsia prediction accuracy in the Han Chinese population, while integration of age information could improve the performance to ~0.75 accuracy using a fivefold training-testing evaluation strategy. CONCLUSIONS: The genetic factors were closely associated with preeclampsia in the Han Chinese population despite large ethnicity heterogeneity. The genotypes of different alleles also had synergetic interactions.

A response prediction model for taxane, cisplatin, and 5-fluorouracil chemotherapy in hypopharyngeal carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. The five-year survival rate of HNSCC has not improved even with major technological advancements in surgery and chemotherapy. Currently, docetaxel, cisplatin, and 5-fluoruracil (TPF) treatment has been the most popular chemotherapy method for HNSCC; but only a small percentage of HNSCC patients exhibit a good response to TPF treatment. Unfortunately, at present, no reasonably effective prediction model exists to assist clinicians with patient treatment. For this reason, patients have no other alternative but to risk neoadjuvant chemotherapy in order to determine their response to TPF. In this study, we analyzed the gene expression profile in TPF-sensitive and non-sensitive patient samples. We identified a gene expression signature between these two groups. We further chose 10 genes and trained a support vector machine (SVM) model. This model has 88.3% sensitivity and 88.9% specificity to predict the response to TPF treatment in our patients. In addition, four more TPF responsive and four more TPF non-sensitive patient samples were used for further validation. This SVM model has been proven to achieve approximately 75.0% sensitivity and 100% specificity to predict TPF response in new patients. This suggests that our 10-genes SVM prediction model has the potential to assist clinicians to personalize treatment for HNSCC patients.

Improvement in prediction of prostate cancer prognosis with somatic mutational signatures.

Prostate cancer is a leading male malignancy worldwide, while the prognosis prediction remains quite inaccurate. The study aimed to observe whether there was an association between the prognosis of prostate cancer and genetic mutation profile, and to build an accurate prognostic predictor based on the genetic signatures. The patients diagnosed of prostate cancer from The Cancer Genomic Atlas were used for prognostic stratification, while the somatic gene mutation profiles were compared between different prognostic groups. The genetic features were further used for training machine-learning models to predict prostate cancer prognosis. No significant gene with somatic mutation rate difference was found between prognostic groups of prostate cancer. Total 43 atypical genes were screened for building a support vector machine model to predict prostate cancer prognosis, with an average accuracy of 66% and 64% for 5-fold cross-validation or training-testing evaluation respectively. When combined with the National Institute for Health and Care Excellence (NICE) features, the model could be further improved, with the 5-fold cross-validation accuracy of ~71%, much better than NICE itself (62%). To our knowledge, for the first time, the research studied the relationship of genome-wide somatic mutations with prostate prognosis, and developed an effective prognostic prediction model with the atypical genetic signatures.

EBT: a statistic test identifying moderate size of significant features with balanced power and precision for genome-wide rate comparisons.

MOTIVATION: In genome-wide rate comparison studies, there is a big challenge for effective identification of an appropriate number of significant features objectively, since traditional statistical comparisons without multi-testing correction can generate a large number of false positives while multi-testing correction tremendously decreases the statistic power. RESULTS: In this study, we proposed a new exact test based on the translation of rate comparison to two binomial distributions. With modeling and real datasets, the exact binomial test (EBT) showed an advantage in balancing the statistical precision and power, by providing an appropriate size of significant features for further studies. Both correlation analysis and bootstrapping tests demonstrated that EBT is as robust as the typical rate-comparison methods, e.g. χ 2 test, Fisher's exact test and Binomial test. Performance comparison among machine learning models with features identified by different statistical tests further demonstrated the advantage of EBT. The new test was also applied to analyze the genome-wide somatic gene mutation rate difference between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), two main lung cancer subtypes and a list of new markers were identified that could be lineage-specifically associated with carcinogenesis of LUAD and LUSC, respectively. Interestingly, three cilia genes were found selectively with high mutation rates in LUSC, possibly implying the importance of cilia dysfunction in the carcinogenesis. AVAILABILITY AND IMPLEMENTATION: An R package implementing EBT could be downloaded from the website freely: http://www.szu-bioinf.org/EBT . CONTACT: wangyj@szu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Distribution and Evolution of Yersinia Leucine-Rich Repeat Proteins.

Leucine-rich repeat (LRR) proteins are widely distributed in bacteria, playing important roles in various protein-protein interaction processes. In Yersinia, the well-characterized type III secreted effector YopM also belongs to the LRR protein family and is encoded by virulence plasmids. However, little has been known about other LRR members encoded by Yersinia genomes or their evolution. In this study, the Yersinia LRR proteins were comprehensively screened, categorized, and compared. The LRR proteins encoded by chromosomes (LRR1 proteins) appeared to be more similar to each other and different from those encoded by plasmids (LRR2 proteins) with regard to repeat-unit length, amino acid composition profile, and gene expression regulation circuits. LRR1 proteins were also different from LRR2 proteins in that the LRR1 proteins contained an E3 ligase domain (NEL domain) in the C-terminal region or an NEL domain-encoding nucleotide relic in flanking genomic sequences. The LRR1 protein-encoding genes (LRR1 genes) varied dramatically and were categorized into 4 subgroups (a to d), with the LRR1a to -c genes evolving from the same ancestor and LRR1d genes evolving from another ancestor. The consensus and ancestor repeat-unit sequences were inferred for different LRR1 protein subgroups by use of a maximum parsimony modeling strategy. Structural modeling disclosed very similar repeat-unit structures between LRR1 and LRR2 proteins despite the different unit lengths and amino acid compositions. Structural constraints may serve as the driving force to explain the observed mutations in the LRR regions. This study suggests that there may be functional variation and lays the foundation for future experiments investigating the functions of the chromosomally encoded LRR proteins of Yersinia.