Deqformer: high-definition and scalable deep learning probe design method.

Target enrichment sequencing techniques are gaining widespread use in the field of genomics, prized for their economic efficiency and swift processing times. However, their success depends on the performance of probes and the evenness of sequencing depth among each probe. To accurately predict probe coverage depth, a model called Deqformer is proposed in this study. Deqformer utilizes the oligonucleotides sequence of each probe, drawing inspiration from Watson-Crick base pairing and incorporating two BERT encoders to capture the underlying information from the forward and reverse probe strands, respectively. The encoded data are combined with a feed-forward network to make precise predictions of sequencing depth. The performance of Deqformer is evaluated on four different datasets: SNP panel with 38 200 probes, lncRNA panel with 2000 probes, synthetic panel with 5899 probes and HD-Marker panel for Yesso scallop with 11 000 probes. The SNP and synthetic panels achieve impressive factor 3 of accuracy (F3acc) of 96.24% and 99.66% in 5-fold cross-validation. F3acc rates of over 87.33% and 72.56% are obtained when training on the SNP panel and evaluating performance on the lncRNA and HD-Marker datasets, respectively. Our analysis reveals that Deqformer effectively captures hybridization patterns, making it robust for accurate predictions in various scenarios. Deqformer leads to a novel perspective for probe design pipeline, aiming to enhance efficiency and effectiveness in probe design tasks.

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.

Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer-IF, across eight distinct human cell lines and exhibits superior performance on generic enhancer datasets and enhancer-promoter datasets, affirming the robustness of Enhancer-MDLF. Additionally, we introduce transfer learning to provide an effective and potential solution to address the prediction challenges posed by enhancer specificity. Furthermore, we utilize model interpretation to identify transcription factor binding site motifs that may be associated with enhancer regions, with important implications for facilitating the study of enhancer regulatory mechanisms. The source code is openly accessible at https://github.com/HaoWuLab-Bioinformatics/Enhancer-MDLF.

DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework.

Microbial community analysis is an important field to study the composition and function of microbial communities. Microbial species annotation is crucial to revealing microorganisms' complex ecological functions in environmental, ecological and host interactions. Currently, widely used methods can suffer from issues such as inaccurate species-level annotations and time and memory constraints, and as sequencing technology advances and sequencing costs decline, microbial species annotation methods with higher quality classification effectiveness become critical. Therefore, we processed 16S rRNA gene sequences into k-mers sets and then used a trained DNABERT model to generate word vectors. We also design a parallel network structure consisting of deep and shallow modules to extract the semantic and detailed features of 16S rRNA gene sequences. Our method can accurately and rapidly classify bacterial sequences at the SILVA database's genus and species level. The database is characterized by long sequence length (1500 base pairs), multiple sequences (428,748 reads) and high similarity. The results show that our method has better performance. The technique is nearly 20% more accurate at the species level than the currently popular naive Bayes-dominated QIIME 2 annotation method, and the top-5 results at the species level differ from BLAST methods by <2%. In summary, our approach combines a multi-module deep learning approach that overcomes the limitations of existing methods, providing an efficient and accurate solution for microbial species labeling and more reliable data support for microbiology research and application.

MLSNet: a deep learning model for predicting transcription factor binding sites.

Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be enhanced. In this study, we propose MLSNet, a novel deep learning architecture designed specifically to predict TFBSs. MLSNet innovatively integrates multisize convolutional fusion with long short-term memory (LSTM) networks to effectively capture DNA-sparse higher-order sequence features. Further, MLSNet incorporates super token attention and Bi-LSTM to systematically extract and integrate higher-order DNA shape features. Experimental results on 165 ChIP-seq (chromatin immunoprecipitation followed by sequencing) datasets indicate that MLSNet consistently outperforms several state-of-the-art algorithms in the prediction of TFBSs. Specifically, MLSNet reports average metrics: 0.8306 for ACC, 0.8992 for AUROC, and 0.9035 for AUPRC, surpassing the second-best methods by 1.82%, 1.68%, and 1.54%, respectively. This research delineates the effectiveness of combining multi-size convolutional layers with LSTM and DNA shape-based features in enhancing predictive accuracy. Moreover, this study comprehensively assesses the variability in model performance across different cell lines and transcription factors. The source code of MLSNet is available at https://github.com/minghaidea/MLSNet.

Low Complexity Regions in Proteins and DNA are Poorly Correlated.

Low complexity sequences (LCRs) are well known within coding as well as non-coding sequences. A low complexity region within a protein must be encoded by the underlying DNA sequence. Here, we examine the relationship between the entropy of the protein sequence and that of the DNA sequence which encodes it. We show that they are poorly correlated whether starting with a low complexity region within the protein and comparing it to the corresponding sequence in the DNA or by finding a low complexity region within coding DNA and comparing it to the corresponding sequence in the protein. We show this is the case within the proteomes of five model organisms: Homo sapiens, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and Arabidopsis thaliana. We also report a significant bias against mononucleic codons in LCR encoding sequences. By comparison with simulated proteomes, we show that highly repetitive LCRs may be explained by neutral, slippage-based evolution, but compositionally biased LCRs with cryptic repeats are not. We demonstrate that other biological biases and forces must be acting to create and maintain these LCRs. Uncovering these forces will improve our understanding of protein LCR evolution.

Genetic diversities in wild and cultivated populations of the two closely-related medical plants species, Tripterygium Wilfordii and T. Hypoglaucum (Celastraceae).

BACKGROUND: The sustainable supply of medicinal plants is important, and cultivating and domesticating them has been suggested as an optimal strategy. However, this can lead to a loss of genetic diversity. Tripterygium wilfordii Hook. f. is a medicinal plant commonly used in traditional Chinese medicine, but its wild populations are dwindling due to excessive harvesting. To protect the species and meet the increasing demand, it is urgent to cultivate it on a large scale. However, distinguishing between T. wilfordii and T. hypoglaucum, two similar species with different medicinal properties, is challenging. Therefore, it is crucial to understand the genetic diversity and population structure of these species for their sustainable utilization. RESULTS: In this study, we investigated the genetic diversity and population structure of the two traditional medicinal semiwoody vines plant species, Tripterygium wilfordii and T. hypoglaucum, including wild and cultivated populations using chloroplast DNA (cpDNA) sequences and microsatellite loci. Our results indicated that the two species maintain a high level of genetic divergence, indicating possible genetic bases for the different contents of bioactive compounds of the two species. T. wilfordii showed lower genetic diversity and less subdivided population structures of both markers than T. hypoglaucum. The potential factors in shaping these interesting differences might be differentiated pollen-to-seed migration rates, interbreeding, and history of population divergence. Analyses of cpDNA and microsatellite loci supported that the two species are genetically distinct entities. In addition, a significant reduction of genetic diversity was observed for cultivated populations of the two species, which mainly resulted from the small initial population size and propagated vegetative practice during their cultivation. CONCLUSION: Our findings indicate significant genetic divergence between T. wilfordii and T. hypoglaucum. The genetic diversity and population structure analyses provide important insights into the sustainable cultivation and utilization of these medicinal plants. Accurate identification and conservation efforts are necessary for both species to ensure the safety and effectiveness of crude drug use. Our study also highlighted the importance of combined analyses of different DNA markers in addressing population genetics of medicinal plants because of the contrasts of inheritance and rates of gene flow. Large-scale cultivation programs should consider preserving genetic diversity to enhance the long-term sustainability of T. wilfordii and T. hypoglaucum. Our study proposed that some populations showed higher genetic diversity and distinctness, which can be considered with priority for conservation and as the sources for future breeding and genetic improvement.

ß-glucans, SAM, and GSH fluctuations in barley anther tissue culture conditions affect regenerants' DNA methylation and GPRE.

BACKGROUND: Microspore embryogenesis is a process that produces doubled haploids in tissue culture environments and is widely used in cereal plants. The efficient production of green regenerants requires stresses that could be sensed at the level of glycolysis, followed by the Krebs cycle and electron transfer chain. The latter can be affected by Cu(II) ion concentration in the induction media acting as cofactors of biochemical reactions, indirectly influencing the production of glutathione (GSH) and S-adenosyl-L-methionine (SAM) and thereby affecting epigenetic mechanisms involving DNA methylation (demethylation-DM, de novo methylation-DNM). The conclusions mentioned were acquired from research on triticale regenerants, but there is no similar research on barley. In this way, the study looks at how DNM, DM, Cu(II), SAM, GSH, and ß-glucan affect the ability of green plant regeneration efficiency (GPRE). RESULTS: The experiment involved spring barley regenerants obtained through anther culture. Nine variants (trials) of induction media were created by adding copper (CuSO4: 0.1; 5; 10 µM) and silver salts (AgNO3: 0; 10; 60 µM), with varying incubation times for the anthers (21, 28, and 35 days). Changes in DNA methylation were estimated using the DArTseqMet molecular marker method, which also detects cytosine methylation. Phenotype variability in ß-glucans, SAM and GSH induced by the nutrient treatments was assessed using tentative assignments based on the Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy. The effectiveness of green plant regeneration ranged from 0.1 to 2.91 plants per 100 plated anthers. The level of demethylation ranged from 7.61 to 32.29, while de novo methylation reached values ranging from 6.83 to 32.27. The paper demonstrates that the samples from specific in vitro conditions (trials) formed tight groups linked to the factors contributing to the two main components responsible for 55.05% of the variance (to the first component DNM, DM, to the second component GSH, ß-glucans, Cu(II), GPRE). CONCLUSIONS: We can conclude that in vitro tissue culture conditions affect biochemical levels, DNA methylation changes, and GPRE. Increasing Cu(II) concentration in the IM impacts the metabolism and DNA methylation, elevating GPRE. Thus, changing Cu(II) concentration in the IM is fair to expect to boost GPRE.

An insight into tissue culture-induced variation origin shared between anther culture-derived triticale regenerants.

BACKGROUND: The development of the plant in vitro techniques has brought about the variation identified in regenerants known as somaclonal or tissue culture-induced variation (TCIV). S-adenosyl-L-methionine (SAM), glutathione (GSH), low methylated pectins (LMP), and Cu(II) ions may be implicated in green plant regeneration efficiency (GPRE) and TCIV, according to studies in barley (Hordeum vulgare L.) and partially in triticale (× Triticosecale spp. Wittmack ex A. Camus 1927). Using structural equation models (SEM), these metabolites have been connected to the metabolic pathways (Krebs and Yang cycles, glycolysis, transsulfuration), but not for triticale. Using metabolomic and (epi)genetic data, the study sought to develop a triticale regeneration efficiency statistical model. The culture's induction medium was supplemented with various quantities of Cu(II) and Ag(I) ions for regeneration. The period of plant regeneration has also changed. The donor plant, anther-derived regenerants, and metAFLP were utilized to analyze TCIV concerning DNA in symmetric (CG, CHG) and asymmetric (CHH) sequence contexts. Attenuated Total Reflectance-Fourier Transfer Infrared (ATR-FTIR) spectroscopy was used to gather the metabolomic information on LMP, SAM, and GSH. To frame the data, a structural equation model was employed. RESULTS: According to metAFLP analysis, the average sequence change in the CHH context was 8.65%, and 0.58% was de novo methylation. Absorbances of FTIR spectra in regions specific for LMP, SAM, and GSH were used as variables values introduced to the SEM model. The average number of green regenerants per 100 plated anthers was 2.55. CONCLUSIONS: The amounts of pectin demethylation, SAM, de novo methylation, and GSH are connected in the model to explain GPRE. By altering the concentration of Cu(II) ions in the medium, which influences the amount of pectin, triticale's GPRE can be increased.

A rapid and scalable approach to build synthetic repetitive hormone-responsive promoters.

Advancement of DNA-synthesis technologies has greatly facilitated the development of synthetic biology tools. However, high-complexity DNA sequences containing tandems of short repeats are still notoriously difficult to produce synthetically, with commercial DNA synthesis companies usually rejecting orders that exceed specific sequence complexity thresholds. To overcome this limitation, we developed a simple, single-tube reaction method that enables the generation of DNA sequences containing multiple repetitive elements. Our strategy involves commercial synthesis and PCR amplification of padded sequences that contain the repeats of interest, along with random intervening sequence stuffers that include type IIS restriction enzyme sites. GoldenBraid molecular cloning technology is then employed to remove the stuffers, rejoin the repeats together in a predefined order, and subclone the tandem(s) in a vector using a single-tube digestion-ligation reaction. In our hands, this new approach is much simpler, more versatile and efficient than previously developed solutions to this problem. As a proof of concept, two different phytohormone-responsive, synthetic, repetitive proximal promoters were generated and tested in planta in the context of transcriptional reporters. Analysis of transgenic lines carrying the synthetic ethylene-responsive promoter 10x2EBS-S10 fused to the GUS reporter gene uncovered several developmentally regulated ethylene response maxima, indicating the utility of this reporter for monitoring the involvement of ethylene in a variety of physiologically relevant processes. These encouraging results suggest that this reporter system can be leveraged to investigate the ethylene response to biotic and abiotic factors with high spatial and temporal resolution.

Bi-allelic variants in MYH3 cause recessively-inherited arthrogryposis.

Arthrogryposis is a clinical feature defined by congenital joint contractures in two or more different body areas which occurs in between 1/3000 and 1/5000 live births. Variants in multiple genes have been associated with distal arthrogryposis syndromes. Heterozygous variants in MYH3 have been identified to cause the dominantly-inherited distal arthrogryposis conditions, Freeman-Sheldon syndrome, Sheldon-Hall syndrome, and multiple pterygium syndrome. In contrast, MYH3 variants underlie both dominantly and recessively inherited Contractures, Pterygia, and Spondylocarpotarsal Fusion syndromes (CPSFS) which are characterized by extensive bony abnormalities in addition to congenital contractures. Here we report two affected sibs with distal arthrogryposis born to unaffected, distantly related parents. Sequencing revealed that both sibs were homozygous for two ultra-rare MYH3 variants, c.3445G>A (p.Glu1149Lys) and c.4760T>C (p.Leu1587Pro). Sequencing and deletion/duplication analysis of 169 other arthrogryposis genes yielded no other compelling candidate variants. This is the first report of biallelic variants in MYH3 being implicated in a distal arthrogryposis phenotype without the additional features of CPSFS. Thus, akin to CPSFS, both dominant and recessively inherited distal arthrogryposis can be caused by variants in MYH3.

The First Iranian Case of Unstable Hemoglobin Santa Ana.

In this report, we describe a 6-year-old girl with a medical history of pallor, mild icterus, anemia, blood transfusion and abnormal hemoglobin variant analysis on capillary electrophoresis. She was referred for further analysis. DNA sequencing of the proband revealed a de novo mutation in Codon 88 (CTG > CCG) of the ß-globin gene (HBB: c.266T > C) in a heterozygous state compatible with hemoglobin Santa Ana, an unstable hemoglobin. This is the first case of Hb Santa Ana from Iran associated with moderate to severe anemia who underwent splenectomy with clinical improvement.

The Clinical Significance of CRNDE Gene Methylation, Polymorphisms, and CRNDEP Micropeptide Expression in Ovarian Tumors.

CRNDE is an oncogene expressed as a long non-coding RNA. However, our team previously reported that the CRNDE gene also encodes a micropeptide, CRNDEP. The amino acid sequence of CRNDEP has recently been revealed by other researchers, too. This study aimed to investigate genetic alterations within the CRNDEP-coding region of the CRNDE gene, methylation profiling of this gene, and CRNDEP expression analysis. All investigations were performed on clinical material from patients with ovarian tumors of diverse aggressiveness. We found that CRNDEP levels were significantly elevated in highly aggressive tumors compared to benign neoplasms. Consistently, a high level of this micropeptide was a negative, independent, prognostic, and predictive factor in high-grade ovarian cancer (hgOvCa) patients. The cancer-promoting role of CRNDE(P), shown in our recent study, was also supported by genetic and epigenetic results obtained herein, revealing no CRNDEP-disrupting mutations in any clinical sample. Moreover, in borderline ovarian tumors (BOTS), but not in ovarian cancers, the presence of a single nucleotide polymorphism in CRNDE, rs115515594, significantly increased the risk of recurrence. Consistently, in BOTS only, the same genetic variant was highly overrepresented compared to healthy individuals. We also discovered that hypomethylation of CRNDE is associated with increased aggressiveness of ovarian tumors. Accordingly, hypomethylation of this gene's promoter/first exon correlated with hgOvCa resistance to chemotherapy, but only in specimens with accumulation of the TP53 tumor suppressor protein. Taken together, these results contribute to a better understanding of the role of CRNDE(P) in tumorigenesis and potentially may lead to improvements in screening, diagnosis, and treatment of ovarian neoplasms.

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.

The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.

Simulation of African and non-African low and high coverage whole genome sequence data to assess variant calling approaches.

Current variant calling (VC) approaches have been designed to leverage populations of long-range haplotypes and were benchmarked using populations of European descent, whereas most genetic diversity is found in non-European such as Africa populations. Working with these genetically diverse populations, VC tools may produce false positive and false negative results, which may produce misleading conclusions in prioritization of mutations, clinical relevancy and actionability of genes. The most prominent question is which tool or pipeline has a high rate of sensitivity and precision when analysing African data with either low or high sequence coverage, given the high genetic diversity and heterogeneity of this data. Here, a total of 100 synthetic Whole Genome Sequencing (WGS) samples, mimicking the genetics profile of African and European subjects for different specific coverage levels (high/low), have been generated to assess the performance of nine different VC tools on these contrasting datasets. The performances of these tools were assessed in false positive and false negative call rates by comparing the simulated golden variants to the variants identified by each VC tool. Combining our results on sensitivity and positive predictive value (PPV), VarDict [PPV = 0.999 and Matthews correlation coefficient (MCC) = 0.832] and BCFtools (PPV = 0.999 and MCC = 0.813) perform best when using African population data on high and low coverage data. Overall, current VC tools produce high false positive and false negative rates when analysing African compared with European data. This highlights the need for development of VC approaches with high sensitivity and precision tailored for populations characterized by high genetic variations and low linkage disequilibrium.

Predicting enhancer-promoter interactions by deep learning and matching heuristic.

Enhancer-promoter interactions (EPIs) play an important role in transcriptional regulation. Recently, machine learning-based methods have been widely used in the genome-scale identification of EPIs due to their promising predictive performance. In this paper, we propose a novel method, termed EPI-DLMH, for predicting EPIs with the use of DNA sequences only. EPI-DLMH consists of three major steps. First, a two-layer convolutional neural network is used to learn local features, and an bidirectional gated recurrent unit network is used to capture long-range dependencies on the sequences of promoters and enhancers. Second, an attention mechanism is used for focusing on relatively important features. Finally, a matching heuristic mechanism is introduced for the exploration of the interaction between enhancers and promoters. We use benchmark datasets in evaluating and comparing the proposed method with existing methods. Comparative results show that our model is superior to currently existing models in multiple cell lines. Specifically, we found that the matching heuristic mechanism introduced into the proposed model mainly contributes to the improvement of performance in terms of overall accuracy. Additionally, compared with existing models, our model is more efficient with regard to computational speed.

CRISPR/Cas-mediated in planta gene targeting: current advances and challenges.

Gene targeting can be used to make modifications at a specific region in a plant's genome and create high-precision tools for plant biotechnology and breeding. However, its low efficiency is a major barrier to its use in plants. The discovery of CRISPR (clustered regularly interspaced short palindromic repeats)-Cas-based site-specific nucleases capable of inducing double-strand breaks in desired loci resulted in the development of novel approaches for plant gene targeting. Several studies have recently demonstrated improvements in gene targeting efficiency through cell-type-specific expression of Cas nucleases, the use of self-amplified gene-targeting-vector DNA, or manipulation of RNA silencing and DNA repair pathways. In this review, we summarize recent advances in CRISPR/Cas-mediated gene targeting in plants and discuss potential efficiency improvements. Increasing the efficiency of gene targeting technology will help pave the way for increased crop yields and food safety in environmentally friendly agriculture.

Cloning, DNA sequence, and expression of flagellins from high and low virulence strains of Edwardsiella tarda and their macrophage-stimulating activities.

Edwardsiella tarda is a causative pathogen of edwardsiellosis in fish. Our previous studies on high (NUF251) and low (NUF194) virulent strains of E. tarda demonstrated that NUF251 strain induced significantly higher levels of NO and TNF-α from fish and mouse macrophages than NUF194 strain. Subsequent studies suggested that a flagellin-like protein secreted from E. tarda might be a responsible factor for the macrophage-stimulating activities. To evaluate the activities of flagellins of E. tarda, in this study, the flagellin genes of NUF251 and NUF194 strains were isolated by PCR and cloned into pQE-30 and pCold I expression vectors, and then the recombinant flagellins of two strains were overexpressed in E. coli JM109 and pG-Tf/BL21, respectively. The molecular weight of the purified recombinant flagellins of NUF251 and NUF194 strains were estimated to be 45 kDa and 37 kDa, respectively on SDS-PAGE analysis. Referring the three-dimensional structure of Salmonella flagellin, which has been reported to have 4 domains (D0, D1, D2, and D3), high sequence homology between two flagellins of E. tarda was observed at conservative domain (D0 and D1) regions, whereas the sequences equivalent to D2 and D3 domains were different, and even equivalent to 57 amino acids were deleted in NUF194. Both recombinant flagellins induced NO production, mRNA expression level of inducible NO synthase (iNOS), and intercellular ROS production in mouse macrophage cell line RAW264.7 cells. Also, the secretion of TNF-α and its mRNA expression level were increased by treatment of both recombinant flagellins. These results indicate that the recombinant flagellins from different virulent E. tarda strains can stimulate macrophages with nearly equal levels as judged by the parameters tested, even though they are differences in the structure and molecular weight, suggesting that conservative D0 and D1 domains are sufficient structural elements for the recombinant flagellins to induce a certain level of macrophage-stimulation in vitro. Further studies are necessary focusing on the role of D2 and D3 domain regions of the recombinant flagellins as macrophage-stimulating agent as well as their influence on host immune system in vivo.

Alterations of the expression of TET2 and DNA 5-hmC predict poor prognosis in Myelodysplastic Neoplasms.

BACKGROUND: Myelodysplastic Neoplasms (MDS) are clonal stem cell disorders characterized by ineffective hematopoiesis and progression to acute myeloid leukemia, myelodysplasia-related (AML-MR). A major mechanism of pathogenesis of MDS is the aberration of the epigenetic landscape of the hematopoietic stem cells and/or progenitor cells, especially DNA cytosine methylation, and demethylation. Data on TET2, the predominant DNA demethylator of the hematopoietic system, is limited, particularly in the MDS patients from India, whose biology may differ since these patients present at a relatively younger age. We studied the expression and the variants of TET2 in Indian MDS and AML-MR patients and their effects on 5-hydroxymethyl cytosine (5-hmC, a product of TET2 catalysis) and on the prognosis of MDS patients. RESULTS: Of the 42 MDS patients, cytogenetics was available for 31 sub-categorized according to the Revised International Prognostic Scoring System (IPSS-R). Their age resembled that of the previous studies from India. Bone marrow nucleated cells (BMNCs) were also obtained from 13 patients with AML-MR, 26 patients with de-novo AML, and 11 subjects with morphologically normal bone marrow. The patients had a significantly lower TET2 expression which was more pronounced in AML-MR and the IPSS-R higher-risk MDS categories. The 5-hmC levels in higher-risk MDS and AML-MR correlated with TET2 expression, suggesting a possible mechanistic role in the loss of TET2 expression. The findings on TET2 and 5-hmC were also confirmed at the tissue level using immunohistochemistry. Pathogenic variants of TET2 were found in 7 of 24 patient samples (29%), spanning across the IPSS-R prognostic categories. One of the variants - H1778R - was found to affect local and global TET2 structure when studied using structural predictions and molecular dynamics simulations. Thus, it is plausible that some pathogenic variants in TET2 can compromise the structure of TET2 and hence in the formation of 5-hmC. CONCLUSIONS: IPSS-R higher-risk MDS categories and AML-MR showed a reduction in TET2 expression, which was not apparent in lower-risk MDS. DNA 5-hmC levels followed a similar pattern. Overall, a decreased TET2 expression and a low DNA 5-hmC level are predictors of advanced disease and adverse outcome in MDS in the population studied, i.e., MDS patients from India.

Luteibacter jiangsuensis blood stream infection: a first case report.

BACKGROUND: Luteibacter jiangsuensis is a gram-negative aerobic bacillus that was first isolated from soil samples at a pesticide factory in China and reported in 2011. Here, we describe the first case of L. jiangsuensis infection in human. CASE PRESENTATION: A 59-year-old Japanese woman undergoing treatment for Crohn's disease was admitted to our hospital with fever. Clinical examination indicated catheter-related bloodstream infection. The catheter was removed and meropenem was initiated. Morphologically identical glucose non-fermentative gram-negative bacilli were detected from two sets of aerobic blood culture and catheter-tip cultures. MALDI-TOF mass spectrometry failed to identify the bacterium, which was later identified as L. jiangsuensis by 16 S rRNA gene sequencing. Antimicrobial susceptibility test revealed that the isolate was resistant to carbapenem, therefore meropenem was switched to intravenous levofloxacin (500 mg/day). After 14 days of treatment with levofloxacin, the patient was discharged. CONCLUSIONS: This is the first case of L. jiangsuensis infection in human. The strain was identified by 16 S rRNA gene sequence analysis.

iEnhancer-MRBF: Identifying enhancers and their strength with a multiple Laplacian-regularized radial basis function network.

An enhancer is a short DNA sequence containing many binding sites of transcription factors that plays a crucial role in the gene expression of major eukaryotes. It is difficult to avoid the time consumption and high cost of experimental methods. Therefore, with the continuous development of genomics, it is an urgent task to identify enhancers and their intensities by computational methods. In this paper, we propose a two-layer model called iEnhancer-MRBF, wherein the first layer is used to identify enhancers, and the identified enhancers are divided into strong enhancers and weak enhancers according to their strength in the second layer. In iEnhancer-MRBF, a new classifier multiple Laplacian-regularized radial basis function network (MLR-RBFN) is proposed, and three feature representation methods, namely, kmer, nucleotide binary profiles (NBP) and ac-cumulated nucleotide frequency (ANF), as well as feature selection, are used to process DNA sequences. The experimental results show that the model is significantly better than the previous prediction models, and the test accuracy rates of the first and second layers of independent datasets are 79.75% and 83.50%, respectively.