Spectrum-effect relationship between HPLC fingerprint and hypoglycemic of litchi leaves (Litchi chinensis Sonn) in vitro.

Litchi chinensis Sonn (Litchi) has been listed in the Chinese Pharmacopeia, and is an economically and medicinally valuable species within the family Sapindaceae. However, the material basis of its pharmacological action and the pharmacodynamic substances associated with its hypoglycemic effect are still unclear. The predominant objective of this study was to establish the fingerprint profile of litchi leaves and to evaluate the relationship between the components of the high-performance liquid chromatography (HPLC) fingerprint of litchi leaves, assess its hypoglycemic effect by measuring α-glucosidase and α-amylase inhibition, and find the spectrum-effect relationship of litchi leaves by bivariate correlation analysis, Grey relational analysis and partial least squares regression analysis. In this study, the fingerprint of litchi leaves was established by HPLC, and a total of 15 common peaks were identified that clearly calibrated eight components, with P1 being gallic acid, P2 being protocatechuic acid, P3 being catechin, P6 being epicatechin, P12 being rutin, P13 being astragalin, P14 being quercetin and P15 being kaempferol. The similarities between the fingerprints of 11 batches of litchi leaves were 0.766-0.979. Simultaneously, the results of the spectrum-effect relationship showed that the chemical constituents represented by peaks P8, P3, P12, P14, P2, P13, and P11 were relevant to the hypoglycemic effect.

Identification and Characterization of the Homeobox Gene Family in Fusarium pseudograminearum Reveal Their Roles in Pathogenicity.

Homeobox transcription factors have been implicated in filamentous growth, conidia formation and virulence in fungal pathogens. However, the presence of the homeobox gene family and their potential influence on pathogenesis in Fusarium pseudograminearum have not been investigated. F. pseudograminearum is an important plant pathogen that causes wheat and barley crown rot. In this study, we performed a genome-wide survey for F. pseudograminearum homeobox genes, and 11 FpHtfs were identified and characterized. Domain analyses revealed that all of these proteins contain a complete homeobox domain that contains three helices. Expression profiles of FpHtf genes at different pathogen stages showed that six FpHtf genes were induced during infection. Further, we generated and characterized FpHtf3 deletion mutants in F. pseudograminearum, showing it was essential for virulence. These results indicated that members of the homeobox gene family are likely involved in F. pseudograminearum pathogenicity. Our work also provides a useful foundation for further studies on the complexity and function of the homeobox gene family in F. pseudograminearum.

The bZIP transcription factor FpAda1 is essential for fungal growth and conidiation in Fusarium pseudograminearum.

Fusarium pseudograminearum is an important pathogen of Fusarium crown rot and Fusarium head blight, which is able to infect wheat and barley worldwide, causing great economic losses. Transcription factors (TFs) of the basic leucine zipper (bZIP) protein family control important processes in all eukaryotes. In this study, we identified a gene, designated FpAda1, encoding a bZIP TF in F. pseudograminearum. The homolog of FpAda1 is also known to affect hyphal growth in Neurospora crassa. Deletion of FpAda1 in F. pseudograminearum resulted in defects in hyphal growth, mycelial branching and conidia formation. Pathogenicity assays showed that virulence of the Δfpada1 mutant was dramatically decreased on wheat coleoptiles and barley leaves. However, wheat coleoptile inoculation assay showed that Δfpada1 could penetrate and proliferate in wheat cells. Moreover, the FpAda1 was required for abnormal nuclear morphology in conidia and transcription of FpCdc2 and FpCdc42. Taken together, these results indicate that FpAda1 is an important transcription factor involved in growth and development in F. pseudograminearum.

The clinical utility of microRNA-21 as novel biomarker for diagnosing human cancers.

With cancer being a major cause of death worldwide, microRNAs (miRNAs) have been investigated as novel and non-invasive biomarkers for cancer diagnosis. Recently, microRNA-21 (miR-21) attracts much attention for its aberrant expression and has been widely studied in various cancers. However, the inconsistent results from studies make it hard to evaluate the diagnostic value of miR-21 in cancer diagnosis, which lead us to conduct this meta-analysis. We conducted a comprehensive literature search in the Medline, Embase, PubMed, CNKI, and Web of Science before July 1, 2014. STATA 12.0 software was used for calculation and statistical analysis. The pooled sensitivity, specificity, positive and negative likelihood ratio (PLR, NLR), and diagnostic odds ratio (DOR) were used to assess the diagnostic performance of miR-21 for cancers. Seventy-three studies in 60 articles were involved in this meta-analysis, with a total of 4684 patients with cancer and 3108 controls. The overall parameters were calculated from all the included studies: sensitivity of 0.78 (95% confidence interval (CI) 0.74-0.81), specificity of 0.83 (95% CI 0.80-0.86), PLR of 4.5 (95% CI 3.8-5.4), NLR of 0.27 (95% CI 0.23-0.32); DOR of 17 (95% CI 12-23), and area under the curve (AUC) of 0.88 (95% CI 0.84-0.90). In addition, we performed subgroup analyses based on ethnicity, cancer types, and sample types. Results from subgroup analysis showed that cancer types and sample types were the sources of heterogeneity in our meta-analysis. The overall diagnostic value of miR-21 is not very high for cancer diagnosis; however, it is affected significantly by the types of cancer and specimen. MiR-21 has a relatively high diagnostic value for detecting breast cancer, and miR-21 assays based on plasma, serum, and tissue achieved relatively higher accuracy.

How pupils of different ages perceive menus denoting metaphorical and metonymic expressions: Insights from eye-tracking.

The "embodied" position on language comprehension proposes that metaphor or metonymy understanding can be represented in a distributed network based on previous sensorimotor experience. The current study attempted to investigate how children understood metaphor and metonymy in the context of daily diet that provided rich sensory experience for children. We implemented an eye-tracking experiment where a 2 × 2 × 2 mixed design was employed. Thirty Chinese pupils aged from 6 to 12 were instructed to appreciate Chinese menus denoting metaphorical or metonymic expressions. Results of eye-tracking indicated that the dish images captioned with metaphorical names held the greatest attention of pupils, particularly for the juniors. Moreover, the inclusion of Chinese pinyin in the menu served as a distractor that reduced pupils' attention to other menu elements. This study adds to the state of the art on the embodied account of language by inspecting how the under-explored children perceived metaphorical and metonymic expressions. The context of everyday diet, abundant in sensory, provides a more vivid scenario for this topic. It also offers a practical insight into how to design menus to invoke particular sensory experience for children who are undergoing both physical and mental development.

Whole-genome sequencing of Fusarium spp. causing sugarcane root rot on both chewing cane and sugar-making cane.

Previously we isolated three Fusarium strains (a F. sacchari strain namely GXUF-1, and another two F. commune strains namely GXUF-2 and GXUF-3), and we verified that GXUF-3 was able to cause sugarcane root rot to the chewing cane cultivar Badila. Considering that Fusarium spp. are a group of widely distributed fungal pathogens, we tested whether these three Fusarium isolates were able to cause root rot to Badila as well as sugar-making cane cultivar (Guitang42), using a suitable inoculation method established based on infection assays using Badila. We found that the three Fusarium strains were able to cause root rot symptoms to both Badila and Guitang42, to different extents. To better investigate the potential pathogenicity mechanisms, we performed Illumina high-throughput sequencing and analyzed the whole genomic sequence data of these three Fusarium strains. The results reveal that the assembly sizes of the three Fusarium strains were in a range of 44.7-48.2 Mb, with G + C contents of 48.0-48.5%, and 14,154-15,175 coding genes. The coding genes were annotated by multiple public databases, and potential pathogenic genes were predicted using proprietary databases (such as PHI, DFVF, CAZy, etc.). Furthermore, based on evolutionary analysis of the coding sequence, we found that contraction and expansion of gene families occurred in the three Fusarium strains. Overall, our results suggest a potential risk that the root rot disease may occur to the sugar-making canes although it was initially spotted from fruit cane, and provide clues to understand the pathogenic mechanisms of Fusarium spp. causing sugarcane root rot.

Rapid identification of chemical profiles in vitro and in vivo of Huan Shao Dan and potential anti-aging metabolites by high-resolution mass spectrometry, sequential metabolism, and deep learning model.

Background: Aging is marked by the gradual deterioration of cells, tissues, and organs and is a major risk factor for many chronic diseases. Considering the complex mechanisms of aging, traditional Chinese medicine (TCM) could offer distinct advantages. However, due to the complexity and variability of metabolites in TCM, the comprehensive screening of metabolites associated with pharmacology remains a significant issue. Methods: A reliable and integrated identification method based on UPLC-Q Exactive-Orbitrap HRMS was established to identify the chemical profiles of Huan Shao Dan (HSD). Then, based on the theory of sequential metabolism, the metabolic sites of HSD in vivo were further investigated. Finally, a deep learning model and a bioactivity assessment assay were applied to screen potential anti-aging metabolites. Results: This study identified 366 metabolites in HSD. Based on the results of sequential metabolism, 135 metabolites were then absorbed into plasma. A total of 178 peaks were identified from the sample after incubation with artificial gastric juice. In addition, 102 and 91 peaks were identified from the fecal and urine samples, respectively. Finally, based on the results of the deep learning model and bioactivity assay, ginsenoside Rg1, Rg2, and Rc, pseudoginsenoside F11, and jionoside B1 were selected as potential anti-aging metabolites. Conclusion: This study provides a valuable reference for future research on the material basis of HSD by describing the chemical profiles both in vivo and in vitro. Moreover, the proposed screening approach may serve as a rapid tool for identifying potential anti-aging metabolites in TCM.

Protein secondary structure prediction based on Wasserstein generative adversarial networks and temporal convolutional networks with convolutional block attention modules.

As an important task in bioinformatics, protein secondary structure prediction (PSSP) is not only beneficial to protein function research and tertiary structure prediction, but also to promote the design and development of new drugs. However, current PSSP methods cannot sufficiently extract effective features. In this study, we propose a novel deep learning model WGACSTCN, which combines Wasserstein generative adversarial network with gradient penalty (WGAN-GP), convolutional block attention module (CBAM) and temporal convolutional network (TCN) for 3-state and 8-state PSSP. In the proposed model, the mutual game of generator and discriminator in WGAN-GP module can effectively extract protein features, and our CBAM-TCN local extraction module can capture key deep local interactions in protein sequences segmented by sliding window technique, and the CBAM-TCN long-range extraction module can further capture the key deep long-range interactions in sequences. We evaluate the performance of the proposed model on seven benchmark datasets. Experimental results show that our model exhibits better prediction performance compared to the four state-of-the-art models. The proposed model has strong feature extraction ability, which can extract important information more comprehensively.

Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory.

Protein secondary structure prediction (PSSP) is a challenging task in computational biology. However, existing models with deep architectures are not sufficient and comprehensive for deep long-range feature extraction of long sequences. This paper proposes a novel deep learning model to improve Protein secondary structure prediction. In the model, our proposed bidirectional temporal convolutional network (BTCN) can extract the bidirectional deep local dependencies in protein sequences segmented by the sliding window technique, the bidirectional long short-term memory (BLSTM) network can extract the global interactions between residues, and our proposed multi-scale bidirectional temporal convolutional network (MSBTCN) can further capture the bidirectional multi-scale long-range features of residues while preserving the hidden layer information more comprehensively. In particular, we also propose that fusing the features of 3-state and 8-state Protein secondary structure prediction can further improve the prediction accuracy. Moreover, we also propose and compare multiple novel deep models by combining bidirectional long short-term memory with temporal convolutional network (TCN), reverse temporal convolutional network (RTCN), multi-scale temporal convolutional network (multi-scale bidirectional temporal convolutional network), bidirectional temporal convolutional network and multi-scale bidirectional temporal convolutional network, respectively. Furthermore, we demonstrate that the reverse prediction of secondary structure outperforms the forward prediction, suggesting that amino acids at later positions have a greater impact on secondary structure recognition. Experimental results on benchmark datasets including CASP10, CASP11, CASP12, CASP13, CASP14, and CB513 show that our methods achieve better prediction performance compared to five state-of-the-art methods.

WG-ICRN: Protein 8-state secondary structure prediction based on Wasserstein generative adversarial networks and residual networks with Inception modules.

Protein secondary structure is the basis of studying the tertiary structure of proteins, drug design and development, and the 8-state protein secondary structure can provide more adequate protein information than the 3-state structure. Therefore, this paper proposes a novel method WG-ICRN for predicting protein 8-state secondary structures. First, we use the Wasserstein generative adversarial network (WGAN) to extract protein features in the position-specific scoring matrix (PSSM). The extracted features are combined with PSSM into a new feature set of WG-data, which contains richer feature information. Then, we use the residual network (ICRN) with Inception to further extract the features in WG-data and complete the prediction. Compared with the residual network, ICRN can reduce parameter calculations and increase the width of feature extraction to obtain more feature information. We evaluated the prediction performance of the model using six datasets. The experimental results show that the WGAN has excellent feature extraction capabilities, and ICRN can further improve network performance and improve prediction accuracy. Compared with four popular models, WG-ICRN achieves better prediction performance.

Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study.

BACKGROUND: Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is a difficult and complicated task to gain insights from multidimensional oral health surveys. OBJECTIVE: We aimed to develop a visualization framework for the visual analytics and deep mining of multidimensional oral health surveys. METHODS: First, diseases and groups were embedded into data portraits based on their multidimensional attributes. Subsequently, group classification and correlation pattern extraction were conducted to explore the correlation features among diseases, behaviors, symptoms, and cognitions. On the basis of the feature mining of diseases, groups, behaviors, and their attributes, a knowledge graph was constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users. RESULTS: A visualization framework was implemented for the exploration of multidimensional oral health surveys. A series of user-friendly interactions were integrated to propose a visual analysis system that can help users further achieve the regulations of oral health conditions. CONCLUSIONS: A visualization framework is provided in this paper with a set of meaningful user interactions integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world data sets demonstrate the effectiveness of our system in the exploration of oral diseases.

Comparative secretomic and proteomic analysis reveal multiple defensive strategies developed by Vibrio cholerae against the heavy metal (Cd2+, Ni2+, Pb2+, and Zn2+) stresses.

Vibrio cholerae is a common waterborne pathogen that can cause pandemic cholera in humans. The bacterium with heavy metal-tolerant phenotypes is frequently isolated from aquatic products, however, its tolerance mechanisms remain unclear. In this study, we investigated for the first time the response of such V. cholerae isolates (n = 3) toward the heavy metal (Cd2+, Ni2+, Pb2+, and Zn2+) stresses by comparative secretomic and proteomic analyses. The results showed that sublethal concentrations of the Pb2+ (200 µg/mL), Cd2+ (12.5 µg/mL), and Zn2+ (50 µg/mL) stresses for 2 h significantly decreased the bacterial cell membrane fluidity, but increased cell surface hydrophobicity and inner membrane permeability, whereas the Ni2+ (50 µg/mL) stress increased cell membrane fluidity (p < 0.05). The comparative secretomic and proteomic analysis revealed differentially expressed extracellular and intracellular proteins involved in common metabolic pathways in the V. cholerae isolates to reduce cytotoxicity of the heavy metal stresses, such as biosorption, transportation and effluxing, extracellular sequestration, and intracellular antioxidative defense. Meanwhile, different defensive strategies were also found in the V. cholerae isolates to cope with different heavy metal damage. Remarkably, a number of putative virulence and resistance-associated proteins were produced and/or secreted by the V. cholerae isolates under the heavy metal stresses, suggesting an increased health risk in the aquatic products.

DLBLS_SS: protein secondary structure prediction using deep learning and broad learning system.

Protein secondary structure prediction (PSSP) is not only beneficial to the study of protein structure and function but also to the development of drugs. As a challenging task in computational biology, experimental methods for PSSP are time-consuming and expensive. In this paper, we propose a novel PSSP model DLBLS_SS based on deep learning and broad learning system (BLS) to predict 3-state and 8-state secondary structure. We first use a bidirectional long short-term memory (BLSTM) network to extract global features in residue sequences. Then, our proposed SEBTCN based on temporal convolutional networks (TCN) and channel attention can capture bidirectional key long-range dependencies in sequences. We also use BLS to rapidly optimize fused features while further capturing local interactions between residues. We conduct extensive experiments on public test sets including CASP10, CASP11, CASP12, CASP13, CASP14 and CB513 to evaluate the performance of the model. Experimental results show that our model exhibits better 3-state and 8-state PSSP performance compared to five state-of-the-art models.

Microbiome analysis and biocontrol bacteria isolation from rhizosphere soils associated with different sugarcane root rot severity.

To explore the causal pathogen and the correlated rhizosphere soil microecology of sugarcane root rot, we sampled the sugarcane root materials displaying different disease severity, and the corresponding rhizosphere soil, for systematic root phenotype and microbial population analyses. We found that with increased level of disease severity reflected by above-ground parts of sugarcane, the total root length, total root surface area and total volume were significantly reduced, accompanied with changes in the microbial population diversity and structure in rhizosphere soil. Fungal community richness was significantly lower in the rhizosphere soil samples from mildly diseased plant than that from either healthy plant, or severely diseased plant. Particularly, we noticed that a peculiar decrease of potential pathogenic fungi in rhizosphere soil, including genera Fusarium, Talaromyces and Neocosmospora, with increased level of disease severity. As for bacterial community, Firmicutes was found to be of the highest level, while Acidobacteria and Chloroflexi of the lowest level, in rhizosphere soil from healthy plant compared to that from diseased plant of different severity. FUNGuild prediction showed that the proportion of saprophytic fungi was higher in the rhizosphere soil of healthy plants, while the proportion of pathogenic fungi was higher in the rhizosphere soil of diseased plants. By co-occurrence network analysis we demonstrated the Bacillus and Burkholderia were in a strong interaction with Fusarium pathogen(s). Consistently, the biocontrol and/or growth-promoting bacteria isolated from the rhizosphere soil were mostly (6 out of 7) belonging to Bacillus and Burkholderia species. By confrontation culture and pot experiments, we verified the biocontrol and/or growth-promoting property of the isolated bacterial strains. Overall, we demonstrated a clear correlation between sugarcane root rot severity and rhizosphere soil microbiome composition and function, and identified several promising biocontrol bacteria strains with strong disease suppression effect and growth-promoting properties.

Identification and Characterization of Auxin/IAA Biosynthesis Pathway in the Rice Blast Fungus Magnaporthe oryzae.

The rice blast fungus Magnaporthe oryzae has been known to produce the phytohormone auxin/IAA from its hyphae and conidia, but the detailed biological function and biosynthesis pathway is largely unknown. By sequence homology, we identified a complete indole-3-pyruvic acid (IPA)-based IAA biosynthesis pathway in M. oryzae, consisting of the tryptophan aminotransferase (MoTam1) and the indole-3-pyruvate decarboxylase (MoIpd1). In comparison to the wild type, IAA production was significantly reduced in the motam1Δ mutant, and further reduced in the moipd1Δ mutant. Correspondingly, mycelial growth, conidiation, and pathogenicity were defective in the motam1Δ and the moipd1Δ mutants to various degrees. Targeted metabolomics analysis further confirmed the presence of a functional IPA pathway, catalyzed by MoIpd1, which contributes to IAA/auxin production in M. oryzae. Furthermore, the well-established IAA biosynthesis inhibitor, yucasin, suppressed mycelial growth, conidiation, and pathogenicity in M. oryzae. Overall, this study identified an IPA-dependent IAA synthesis pathway crucial for M. oryzae mycelial growth and pathogenic development.

Analysis of Apoptosis-Related Genes Reveals that Apoptosis Functions in Conidiation and Pathogenesis of Fusarium pseudograminearum.

Apoptosis, a type of programmed cell death, plays crucial roles in various physiological processes, from development to adaptive responses. Key features of apoptosis have been verified in various fungal microbes but not yet in Fusarium species. Here, we identified 19 apoptosis-related genes in Fusarium pseudograminearum using a genome-wide survey. Expression profile analysis revealed that several apoptosis-related genes were significantly increased during conidiation and infection stages. Among these is FpBIR1, with two BIR (baculovirus inhibitor-of-apoptosis protein repeat) domains at the N-terminal end of the protein, a homolog of Saccharomyces cerevisiae BIR1, which is a unique apoptosis inhibitor. FpNUC1 is the ortholog of S. cerevisiae NUC1, which triggers AIF1- or YCA1-independent apoptosis. The functions of these two proteins were assessed by creating Δfpbir1 and Δfpnuc1 mutants via targeted gene deletion. The Δfpbir1 mutant had more cells with nuclear fragmentation and exhibited reduced conidiation, conidial formation, and infectivity. Correspondingly, the Δfpnuc1 mutant contained multiple nuclei, produced thicker and more branched hyphae, was reduced in conidiation, and exhibited faster conidial formation and higher infection rates. Taken together, our results indicate that the apoptosis-related genes FpBIR1 and FpNUC1 function in conidiation, conidial germination, and infection by F. pseudograminearumIMPORTANCE The plant-pathogenic fungus F. pseudograminearum is the causal agent of Fusarium crown rot (FCR) in wheat and barley, resulting in substantial yield losses worldwide. Particularly, in the Huanghuai wheat-growing region of China, F. pseudograminearum was reported as the dominant Fusarium species in FCR infections. Apoptosis is an evolutionarily conserved mechanism in eukaryotes, playing crucial roles in development and cell responses to biotic and abiotic stresses. However, few reports on apoptosis in plant fungal pathogens have been published. In this study, we identified 19 conserved apoptosis-related genes in F. pseudograminearum, several of which were significantly increased during conidiation and infection stages. Potential apoptosis functions were assessed by deletion of the putative apoptosis inhibitor gene FpBIR1 and apoptosis trigger gene FpNUC1 in F. pseudograminearum The FpBIR1 deletion mutant exhibited defects in conidial germination and pathogenicity, whereas the FpNUC1 deletion mutant experienced faster conidial formation and higher infection rates. Apoptosis appears to negatively regulate the conidial germination and pathogenicity of F. pseudograminearum To our knowledge, this study is the first report of apoptosis contributing to infection-related morphogenesis and pathogenesis in F. pseudograminearum.

The ER Lumenal Hsp70 Protein FpLhs1 Is Important for Conidiation and Plant Infection in Fusarium pseudograminearum.

Heat shock protein 70s (Hsp70s) are a class of molecular chaperones that are highly conserved and ubiquitous in organisms ranging from microorganisms to plants and humans. Hsp70s play key roles in cellular development and protecting living organisms from environmental stresses such as heat, drought, salinity, acidity, and cold. However, their functions in pathogenic fungi are largely unknown. Here, a total of 14 FpHsp70 genes were identified in Fusarium pseudograminearum, including 3 in the mitochondria, 7 in the cytoplasm, 2 in the endoplasmic reticulum (ER), 1 in the nucleus, and 1 in the plastid. However, the exon-intron boundaries and protein motifs of the FpHsp70 have no consistency in the same subfamily. Expression analysis revealed that most FpHsp70 genes were up-regulated during infection, implying that FpHsp70 genes may play important roles in F. pseudograminearum pathogenicity. Furthermore, knockout of an ER lumenal Hsp70 homolog FpLhs1 gene reduced growth, conidiation, and pathogenicity in F. pseudograminearum. These mutants also showed a defect in secretion of some proteins. Together, FpHsp70s might play essential roles in F. pseudograminearum and FpLhs1 is likely to act on the development and virulence by regulating protein secretion.

Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method.

Protein secondary structure prediction is one of the most important and challenging problems in bioinformatics. Machine learning techniques have been applied to solve the problem and have gained substantial success in this research area. However there is still room for improvement toward the theoretical limit. In this paper, we present a novel method for protein secondary structure prediction based on a data partition and semi-random subspace method (PSRSM). Data partitioning is an important strategy for our method. First, the protein training dataset was partitioned into several subsets based on the length of the protein sequence. Then we trained base classifiers on the subspace data generated by the semi-random subspace method, and combined base classifiers by majority vote rule into ensemble classifiers on each subset. Multiple classifiers were trained on different subsets. These different classifiers were used to predict the secondary structures of different proteins according to the protein sequence length. Experiments are performed on 25PDB, CB513, CASP10, CASP11, CASP12, and T100 datasets, and the good performance of 86.38%, 84.53%, 85.51%, 85.89%, 85.55%, and 85.09% is achieved respectively. Experimental results showed that our method outperforms other state-of-the-art methods.

[Genetic polymorphism analysis of 8 short tandem repeat loci on the X chromosome in Luoba population of China].

OBJECTIVE: To investigate the alleles and genotype frequency of 8 short tandem repeat (STR) loci (DXS6804, DXS8378, DXS7132, DXS7133, DXS6789, DXS101, HPRTB and DXS7423) on the X chromosome in Luoba ethnic group of China. METHODS: The eight X chromosome STR (X-STR) loci were analyzed by polymerase chain reaction (PCR) followed by polyacylamide gel electrophoresis and silver stain. RESULTS: Among 96 unrelated Luoba individuals, the numbers of alleles in the 8 X-STR loci (DXS7133, DXS6789, DXS6804, DXS8378, DXS101, DXS7424, DXS7132 and HPRTB) were 5, 8, 7, 5, 8, 8, 8 and 5, respectively. The genotype frequencies in females were in accordance with Hardy-Weinberg equilibrium. CONCLUSION: The eight X-STR loci are appropriate for individual identification, paternity testing involving a female child and studies on related diseases.

Y chromosome STR haplotypes of Tibetan Living Tibet Lassa.

Allele frequencies and haplotypes of the 17 Y-chromosome STRs loci, namely DYS19, DYS385a/b, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, DYS437, DYS438, DYS439, DYS448, DYS456, DYS458, DYS635 (YGATA C4), and YGATA H4 were determined in a sample of 131 healthy unrelated males from the Lassa area of Tibet Autonomy Region of China (SW China). In 131 samples 106 different haplotypes were encountered, of which 105 were observed only once. The overall haplotype diversity was 0.9998. The results demonstrate that these loci will be very useful for human identification in forensic cases and paternity tests in the Lassa region.