Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.

Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.

Structural annotation, semi-quantification and toxicity prediction of pyrrolizidine alkaloids from functional food: In silico and molecular networking strategy.

Many traditional Chinese herbs contain pyrrolizidine alkaloids (PAs), which have been reported to be toxic to livestock and humans. However, the lack of PAs standards makes it difficult to effectively conduct a risk assessment in the varied components of traditional Chinese medicine. It is necessary to propose a suitable strategy to obtain the representative occurrence data of PAs in complex systems. A comprehensive approach for annotating the structures, concentration, and mutagenicity of PAs in three Chinese herbs has been proposed in this article. First, feature-based molecular networking (FBMN) combined with network annotation propagation (NAP) on the Global Natural Products Social Molecular Networking web platform speeds up the process of annotating PAs found in Chinese herbs. Second, a semi-quantitative prediction model based on the quantitative structure and ionization intensity relationship (QSIIR) is used to forecast the amounts of PAs in complex substrates. Finally, the T.E.S.T. was used to provide predictions regarding the mutagenicity of annotated PAs. The goal of this study was to develop a strategy for combining the results of several computer models for PA screening to conduct a comprehensive analysis of PAs, which is a crucial step in risk assessment of unknown PAs in traditional Chinese herbal preparations.

Dynamically monitoring cellular γ-H2AX reveals the potential of carcinogenicity evaluation for genotoxic compounds.

Amongst all toxicological endpoints, carcinogenicity might pose the greatest concern. Genetic damage has been considered an important underlying mechanism for the carcinogenicity of chemical substances. The demand for in vitro genotoxic tests as alternative approaches is growing rapidly with the implementation of new regulations for compounds. However, currently available in vitro genotoxicity tests are often limited by relatively high false positive rates. Moreover, few studies have explored carcinogenicity potential by in vitro genotoxicity testing due to the shortage of suitable toxicological biomarkers to link gene damage with cancer risk. γ-H2AX is a recently acknowledged attractive endpoint (biomarker) for evaluating DNA damage and can simultaneously reflect the DNA damage response and repair of cells. We previously reported an ultrasensitive and reliable method, namely stable-isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS), for detecting cellular γ-H2AX and evaluating genotoxic chemicals. More importantly, our method can dynamically monitor the specific processes of genotoxic compounds affecting DNA damage and repair reflected by the amount of γ-H2AX. To clarify the possibility of using this method to assess the potential carcinogenicity of genotoxic chemicals, we applied it to a set of 69 model compounds recommended by the European Center for the Validation of Alternative Methods (ECVAM), with already-characterized genotoxic potential. Compared to conventional in vitro genotoxicity assays, including the Ames test, the γ-H2AX assay by MS has high accuracy (94-96%) due to high sensitivity and specificity (88% and 100%, respectively). The dynamic profiles of model compounds after exposure in HepG2 cells were explored, and a mathematical approach was employed to simulate and quantitatively model the DNA repair kinetics of genotoxic carcinogens (GCs) based on γ-H2AX time-effect curves up to 8 h. Two crucial parameters, i.e., k (rate of γ-H2AX decay) and t50 (time required for γ-H2AX from maximum decrease to half) estimated by the least squares method, were achieved. An open web server to help researchers calculate these two key parameters and profile simulated curves of the tested compound is available online ( http://ccb1.bmi.ac.cn:81/shiny-server/sample-apps/prediction1/ ). We detected a positive association between carcinogenic levels and k and t50 values of γ-H2AX in tested GCs, validating the potential of using this MS-based γ-H2AX in vitro assay to help preliminarily evaluate carcinogenicity and assess genotoxicity. This approach may be used alone or integrated into an existing battery of in vitro genetic toxicity tests.

Genome-wide analysis of lncRNA stability in human.

Transcript stability is associated with many biological processes, and the factors affecting mRNA stability have been extensively studied. However, little is known about the features related to human long noncoding RNA (lncRNA) stability. By inhibiting transcription and collecting samples in 10 time points, genome-wide RNA-seq studies was performed in human lung adenocarcinoma cells (A549) and RNA half-life datasets were constructed. The following observations were obtained. First, the half-life distributions of both lncRNAs and messanger RNAs (mRNAs) with one exon (lnc-human1 and m-human1) were significantly different from those of both lncRNAs and mRNAs with more than one exon (lnc-human2 and m-human2). Furthermore, some factors such as full-length transcript secondary structures played a contrary role in lnc-human1 and m-human2. Second, through the half-life comparisons of nucleus- and cytoplasm-specific and common lncRNAs and mRNAs, lncRNAs (mRNAs) in the nucleus were found to be less stable than those in the cytoplasm, which was derived from transcripts themselves rather than cellular location. Third, kmers-based protein-RNA or RNA-RNA interactions promoted lncRNA stability from lnc-human1 and decreased mRNA stability from m-human2 with high probability. Finally, through applying deep learning-based regression, a non-linear relationship was found to exist between the half-lives of lncRNAs (mRNAs) and related factors. The present study established lncRNA and mRNA half-life regulation networks in the A549 cell line and shed new light on the degradation behaviors of both lncRNAs and mRNAs.

Deep learning methods improve linear B-cell epitope prediction.

BACKGROUND: B-cell epitopes play important roles in vaccine design, clinical diagnosis, and antibody production. Although some models have been developed to predict linear or conformational B-cell epitopes, their performance is still unsatisfactory. Hundreds of thousands of linear B-cell epitope data have accumulated in the Immune Epitope Database (IEDB). These data can be explored using the deep learning methods, in order to create better predictive models for linear B-cell epitopes. RESULTS: After data cleaning, we obtained 240,563 peptide samples with experimental evidence from the IEDB database, including 25,884 linear B-cell epitopes and 214,679 non-epitopes. Based on the peptide center, we adapted each peptide to the same length by trimming or extending. A random portion of the data, with the same amount of epitopes and non-epitopes, were set aside as test dataset. Then a same number of epitopes and non-epitopes were randomly selected from the remaining data to build a classifier with the feedforward deep neural network. We built eleven classifiers to form an ensemble prediction model. The model will report a peptide as an epitope if it was classified as epitope by all eleven classifiers. Then we used the test data set to evaluate the performance of the model using the area value under the receiver operating characteristic (ROC) curve (AUC) as an indicator. We established 40 models to predict linear B-cell epitopes of length from 11 to 50 separately, and found that the AUC value increased with the length and tended to be stable when the length was 38. Repeated results showed that the models constructed by this method were robust. Tested on our and two public test datasets, our models outperformed current major models available. CONCLUSIONS: We applied the feedforward deep neural network to the large amount of linear B-cell epitope data with experimental evidence in the IEDB database, and constructed ensemble prediction models with better performance than the current major models available. We named the models as DLBEpitope and provided web services using the models at http://ccb1.bmi.ac.cn:81/dlbepitope/.

Immune receptor repertoires in pediatric and adult acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML), caused by the abnormal proliferation of immature myeloid cells in the blood or bone marrow, is one of the most common hematologic malignancies. Currently, the interactions between malignant myeloid cells and the immune microenvironment, especially T cells and B cells, remain poorly characterized. METHODS: In this study, we systematically analyzed the T cell receptor and B cell receptor (TCR and BCR) repertoires from the RNA-seq data of 145 pediatric and 151 adult AML samples as well as 73 non-tumor peripheral blood samples. RESULTS: We inferred over 225,000 complementarity-determining region 3 (CDR3) sequences in TCR α, ß, γ, and δ chains and 1,210,000 CDR3 sequences in B cell immunoglobulin (Ig) heavy and light chains. We found higher clonal expansion of both T cells and B cells in the AML microenvironment and observed many differences between pediatric and adult AML. Most notably, adult AML samples have significantly higher level of B cell activation and more secondary Ig class switch events than pediatric AML or non-tumor samples. Furthermore, adult AML with highly expanded IgA2 B cells, which might represent an immunosuppressive microenvironment, are associated with regulatory T cells and worse overall survival. CONCLUSIONS: Our comprehensive characterization of the AML immune receptor repertoires improved our understanding of T cell and B cell immunity in AML, which may provide insights into immunotherapies in hematological malignancies.

Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model.

BACKGROUND: Compared with clinically functioning pituitary adenoma (FPA), clinically non-functioning pituitary adenoma (NFPA) lacks of detectable hypersecreting serum hormones and related symptoms which make it difficult to predict the prognosis and monitoring for postoperative tumour regrowth. We aim to investigate whether the expression of selected tumour-related proteins and clinical features could be used as tumour markers to effectively predict the regrowth of NFPA. METHOD: Tumour samples were collected from 295 patients with NFPA from Beijing Tiantan Hospital. The expression levels of 41 tumour-associated proteins were assessed using tissue microarray analyses. Clinical characteristics were analysed via univariate and multivariate logistic regression analyses. Logistic regression algorithm was applied to build a prediction model based on the expression levels of selected proteins and clinical signatures, which was then assessed in the testing set. RESULTS: Three proteins and two clinical signatures were confirmed to be significantly related to the regrowth of NFPA, including cyclin-dependent kinase inhibitor 2A (CDKN2A/p16), WNT inhibitory factor 1 (WIF1), tumour growth factor beta (TGF-ß), age and tumour volume. A prediction model was generated on the training set, which achieved a fivefold predictive accuracy of 81.2%. The prediction ability was validated on the testing set with an accuracy of 83.9%. The area under the receiver operating characteristic curves (AUC) for the signatures were 0.895 and 0.881 in the training and testing sets, respectively. CONCLUSION: The prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions.

Up-regulation of FAM64A promotes epithelial-to-mesenchymal transition and enhances stemness features in breast cancer cells.

FAM64A was found to be markedly up-regulated in tumor samples and associated with worse overall survival in multiple cancer types, including breast cancer. However, the functional significance of FAM64A in breast cancer remains largely unknown. In this study, we systematically investigated the expression of FAM64A in multiple public breast cancer datasets. We found that FAM64A is significantly positively correlated with tumor stemness index in breast cancer samples, corresponding with an advanced clinical grade, metastasis and unfavorable prognosis. In vitro experiments further showed an up-regulation of stemness genes after over-expressing FAM64A in breast cancer cells. FAM64A overexpression also promoted breast cancer cell proliferation, migration, accompanied by the activation of epithelial-to-mesenchymal transition (EMT). Besides, we identified a strong association of FAM64A expression with TP53 mutations in TCGA and three additional breast cancer datasets. In summary, our study revealed a novel function of FAM64A in promoting breast cancer stemness and EMT, suggesting that targeting of FAM64A may have therapeutic values in advanced breast cancer.

Detecting RNA-RNA interactions in E. coli using a modified CLASH method.

BACKGROUND: Bacterial small regulatory RNAs (sRNAs) play important roles in sensing environment changes through sRNA-target mRNA interactions. However, the current strategy for detecting sRNA-mRNA interactions usually combines bioinformatics prediction and experimental verification, which is hampered by low prediction accuracy and low-throughput. Additionally, among the 4736 sequenced bacterial genomes, only about 2164 sRNAs from 319 strains have been described. Furthermore, target mRNAs of only 157 sRNAs have been uncovered. Obviously, highly efficient methods were required to detect sRNA-mRNA interactions in the sequenced genomes. This study aimed to apply a modified CLASH (cross-linking, ligation and sequencing hybrids) method to detect RNA-RNA interactions in E. coli, a model bacterial organism. RESULTS: Statistically significant interactions were detected in 29 transcript pairs. To the best of our knowledge, 24 pairs were reported for the first time and were novel RNA interactions, including tRNA-tRNA, tRNA-ncRNA (non-coding RNA), tRNA-rRNA, rRNA-mRNA, rRNA-ncRNA, rRNA-rRNA, rRNA-IGT (intergenic transcript), and tRNA-IGT interactions. CONCLUSIONS: Discovery of novel RNA-RNA interactions in the present study demonstrates that RNA-RNA interactions might be far more complicated than ever expected. New methods may be required to help discover more novel RNA-RNA interactions. The present work describes a high-throughput protocol not only for discovering new RNA interactions, but also directly obtaining base-pairing sequences, which should be useful in assessing RNA structure and interactions.

A Novel Small RNA Regulates Tolerance and Virulence in Shigella flexneri by Responding to Acidic Environmental Changes.

Shigella flexneri is an important cause of bacillary dysentery in developing countries. Small regulatory RNAs (sRNAs) play essential roles in diverse cellular processes. We found a novel sRNA Ssr1 based on RT-PCR, northern blot, and 5'RACE in S. flexneri. Ssr1 responds to acidic environmental changes, as shown by a strong linear correlation between the pH value and Ssr1 expression (R = 0.785, P < 0.05) using the qRT-PCR method. Deletion of Ssr1 results in growth retardation at pH values ranging from 5.0 to 7.0 (P < 0.05), and the survival rate was reduced by 22% in acidic conditions (pH 3.0). Additionally, virulence was significantly increased in an Ssr1 mutant strain, as revealed in a murine lung invasion model and survival model assays. By using the sTarPicker method and proteomic analysis, we considered that DnaK, which is a major factor that confers acidic stress tolerance, may be a direct target of Ssr1. We also found that Ssr1 may enhance virulence by directly targeting OmpA; this leads to altered expression of genes in the type three secretion system (T3SS). This work provides new insight into the mechanism of adaptation to environmental stress and into the pathogenesis of Shigella.

sRNATarBase 3.0: an updated database for sRNA-target interactions in bacteria.

Bacterial sRNAs are a class of small regulatory RNAs of about 40-500 nt in length; they play multiple biological roles through binding to their target mRNAs or proteins. Therefore, elucidating sRNA targets is very important. However, only targets of a few sRNAs have been described. To facilitate sRNA functional studies such as developing sRNA target prediction models, we updated the sRNATarBase database, which was initially developed in 2010. The new version (recently moved to http://ccb1.bmi.ac.cn/srnatarbase/) contains 771 sRNA-target entries manually collected from 213 papers, and 23 290 and 11 750 predicted targets from sRNATarget and sTarPicker, respectively. Among the 771 entries, 475 and 17 were involved in validated sRNA-mRNA and sRNA-protein interactions, respectively, while 279 had no reported interactions. We also presented detailed information for 316 binding regions of sRNA-target mRNA interactions and related mutation experiments, as well as new features, including NCBI sequence viewer, sRNA regulatory network, target prediction-based GO and pathway annotations, and error report system. The new version provides a comprehensive annotation of validated sRNA-target interactions, and will be a useful resource for bacterial sRNA studies.

Altered superficial amygdala-cortical functional link in resting state after 36 hours of total sleep deprivation.

The superficial amygdala (SFA) is important in human emotion/affective processing via its strong connection with other limbic and cerebral cortex for receptive and expressive emotion processing. Few studies have investigated the functional connectivity changes of the SFA under extreme conditions, such as prolonged sleep loss, although the SFA showed a distinct functional connectivity pattern throughout the brain. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) was employed to investigate the changes of SFA-cortical functional connectivity after 36 hr of total sleep deprivation (TSD). Fourteen healthy male volunteers aged 25.9 ± 2.3 years (range 18-28 years) enrolled in this within-subject crossover study. We found that the right SFA showed increased functional connectivity with the right medial prefrontal cortex (mPFC) and decreased functional connectivity with the right dorsal posterior cingulate cortex (dPCC) in the resting brain after TSD compared with that during rested wakefulness. For the left SFA, decreased connectivity with the right dorsal anterior cingulate cortex (dACC) and right dPCC was found. Further regression analysis indicated that the functional link between mPFC and SFA significantly correlated with the Profile of Mood State scores. Our results suggest that the amygdala cannot be treated as a single unit in human neuroimaging studies and that TSD may alter the functional connectivity pattern of the SFA, which in turn disrupts emotional regulation.

Large-Scale Brain Network Coupling Predicts Total Sleep Deprivation Effects on Cognitive Capacity.

Interactions between large-scale brain networks have received most attention in the study of cognitive dysfunction of human brain. In this paper, we aimed to test the hypothesis that the coupling strength of large-scale brain networks will reflect the pressure for sleep and will predict cognitive performance, referred to as sleep pressure index (SPI). Fourteen healthy subjects underwent this within-subject functional magnetic resonance imaging (fMRI) study during rested wakefulness (RW) and after 36 h of total sleep deprivation (TSD). Self-reported scores of sleepiness were higher for TSD than for RW. A subsequent working memory (WM) task showed that WM performance was lower after 36 h of TSD. Moreover, SPI was developed based on the coupling strength of salience network (SN) and default mode network (DMN). Significant increase of SPI was observed after 36 h of TSD, suggesting stronger pressure for sleep. In addition, SPI was significantly correlated with both the visual analogue scale score of sleepiness and the WM performance. These results showed that alterations in SN-DMN coupling might be critical in cognitive alterations that underlie the lapse after TSD. Further studies may validate the SPI as a potential clinical biomarker to assess the impact of sleep deprivation.

Predicting linear B-cell epitopes using amino acid anchoring pair composition.

BACKGROUND: Accurate identification of linear B-cell epitopes plays an important role in peptide vaccine designs, immunodiagnosis, and antibody productions. Although several prediction methods have been reported, unsatisfied accuracy has limited the broad usages in linear B-cell epitope prediction. Therefore, developing a reliable model with significant improvement on prediction accuracy is highly desirable. RESULTS: In this study, we developed a novel model for prediction of linear B-cell epitopes, APCpred, which was derived from the combination of amino acid anchoring pair composition (APC) and Support Vector Machine (SVM) methods. Systematic comparisons with the existing prediction models demonstrated that APCpred method significantly improved the prediction accuracy both in fivefold cross-validation of training datasets and in independent blind datasets. In the fivefold cross-validation test with Chen872 dataset at window size of 20, APCpred achieved AUC of 0.809 and accuracy of 72.94%, which was much more accurate than the existing models, e.g., Bayesb, Chen's AAP methods and the enhanced combination method of AAP with five AP scales. For the fivefold cross-validation test with ABC16 dataset, APCpred achieved an improved AUC of 0.794 and ACC of 73.00% at window size of 16, and attained an AUC of 0.748 and ACC of 67.96% on Blind387 dataset after being trained with ABC16 dataset. Trained with Lbtope_Confirm dataset, APCpred achieved an increased Acc of 55.09% on FBC934 dataset. Within sequence window sizes from 12 to 20, APCpred final model on homology-reduced dataset achieved an optimal AUC of 0.748 and ACC of 68.43% in fivefold cross-validation at the window size of 20. CONCLUSION: APCpred model demonstrated a significant improvement in predicting linear B-cell epitopes using the features of amino acid anchoring pair composition (APC). Based on our study, a webserver has been developed for on-line prediction of linear B-cell epitopes, which is a free access at: http:/ccb.bmi.ac.cn/APCpred/.

hfq regulates acid tolerance and virulence by responding to acid stress in Shigella flexneri.

Shigella flexneri is an important etiological agent of bacillary dysentery in developing countries. The Hfq protein is thought to play a major regulatory role in various cellular processes in this organism. However, the roles of Hfq in stress tolerance and virulence in S. flexneri in response to environmental stress have not been fully studied. In this study, hfq was highly expressed when S. flexneri was exposed to low pH. Growth retardation was observed in the hfq deletion mutant at pH values ranging from 5.0 to 7.0 and the survival rate of the mutant strain was reduced by 60% in acidic conditions (pH 3.0) compared with the wild-type strain. Additionally, competitive invasion assays in HeLa cells and lung invasion assays showed that the virulence of the hfq deletion mutant was significantly decreased. An evaluation of the mechanism revealed that, along with the expression of the Type III secretion system genes, acid resistance genes were also increased with acid stress. Interestingly, a statistically strong linear correlation was observed between the expression of hfq and Type III secretion system genes, as well as between hfq and acid resistance genes, under various pH conditions. In this study, we provide evidence that Hfq regulates genes related to acid resistance for survival under acid stress and controls virulence through the positive regulation of Type III secretion systems. Importantly, we propose that hfq is a key factor in maximal adaptation to host acid stress during infection, regulating acid stress tolerance and virulence in response to acid stress in S. flexneri.

Nedaplatin salvage chemotherapy for cervical cancer.

PURPOSE: This systematic analysis was conducted to evaluate the efficacy and safety of nedaplatin based salvage chemotherapy for treatment of patients with advanced cervical cancer. METHODS: Clinical studies evaluating the efficacy and safety of nedaplatin based regimens on response and safety for patients with cervical cancer were identified using a predefined search strategy. Pooled response rates (RRs) were calculated. RESULTS: For nedaplatin based regimens, 5 clinical studies including 264 patients with advanced cervical cancer were considered eligible for inclusion. The analysis showed that, in all patients, pooled RR was 74.6% (197/264). Major adverse effects were leukopenia, thrombocytopenia and nausea/vomiting. No treatment related death occurred with nedaplatin based treatment. CONCLUSION: This systematic analysis suggests that nedaplatin based regimens are associated with good activity with acceptable tolerability in treating patients with advanced cervical cancer.

The potential biomarker panels for identification of Major Depressive Disorder (MDD) patients with and without early life stress (ELS) by metabonomic analysis.

OBJECTIVE: The lack of the disease biomarker to support objective laboratory tests still constitutes a bottleneck in the clinical diagnosis and evaluation of major depressive disorder (MDD) and its subtypes. We used metabonomic techniques to screen the diagnostic biomarker panels from the plasma of MDD patients with and without early life stress (ELS) experience. METHODS: Plasma samples were collected from 25 healthy adults and 46 patients with MDD, including 23 patients with ELS and 23 patients without ELS. Furthermore, gas chromatography/mass spectrometry (GC/MS) coupled with multivariate statistical analysis was used to identify the differences in global plasma metabolites among the 3 groups. RESULTS: The distinctive metabolic profiles exist either between healthy subjects and MDD patients or between the MDD patients with ELS experience (ELS/MDD patients) and the MDD patients without it (non-ELS/MDD patients), and some diagnostic panels of feature metabolites' combination have higher predictive potential than the diagnostic panels of differential metabolites. CONCLUSIONS: These findings in this study have high potential of being used as novel laboratory diagnostic tool for MDD patients and it with ELS or not in clinical application.

Identification of a tumor-suppressive human-specific microRNA within the FHIT tumor-suppressor gene.

Loss or attenuated expression of the tumor-suppressor gene FHIT is associated paradoxically with poor progression of human tumors. Fhit promotes apoptosis and regulates reactive oxygen species; however, the mechanism by which Fhit inhibits tumor growth in animals remains unclear. In this study, we used a multidisciplinary approach based on bioinformatics, small RNA library screening, human tissue analysis, and a xenograft mouse model to identify a novel member of the miR-548 family in the fourth intron of the human FHIT gene. Characterization of this human-specific microRNA illustrates the importance of this class of microRNAs in tumor suppression and may influence interpretation of Fhit action in human cancer.

Predicting sRNAs and their targets in bacteria.

Bacterial small RNAs (sRNAs) are an emerging class of regulatory RNAs of about 40-500 nucleotides in length and, by binding to their target mRNAs or proteins, get involved in many biological processes such as sensing environmental changes and regulating gene expression. Thus, identification of bacterial sRNAs and their targets has become an important part of sRNA biology. Current strategies for discovery of sRNAs and their targets usually involve bioinformatics prediction followed by experimental validation, emphasizing a key role for bioinformatics prediction. Here, therefore, we provided an overview on prediction methods, focusing on the merits and limitations of each class of models. Finally, we will present our thinking on developing related bioinformatics models in future.

A modified visual loop-mediated isothermal amplification method for diagnosis and differentiation of main pathogens from Mycobacterium tuberculosis complex.

This study was aimed to rapidly identify and differentiate two main pathogens of the Mycobacterium tuberculosis complex: Mycobacterium tuberculosis subsp. tuberculosis and Mycobacterium bovis by a modified loop-mediated isothermal amplification (LAMP) assay. The reaction results could be evaluated by naked eye with two optimized closed tube detection methods as follows: adding the modified fluorescence dye in advance into the reaction mix so as to observe the color changes or putting a tinfoil in the tube and adding the SYBR Green I dye on it, then making the dye drop into the bottom of the tube by centrifuge after reaction. The results showed that the two groups of primers used jointly in this assay could successfully identify and differentiate Mycobacterium tuberculosis subsp. tuberculosis and Mycobacterium tuberculosis bovis. Sensitivity test displayed that the modified LAMP assay with the closed tube system could determine the minimal template concentration of 1 copy/µl, which was more sensitive than that of routine PCR. The advantages of this LAMP method for detection of the Mycobacterium tuberculosis complex included high specificity, high sensitivity, simplicity, and superiority in avoidance of aerosol contamination. The modified LAMP assay would provide a potential for clinical diagnosis and therapy of tuberculosis in the developing countries and the resource-limited areas.