Tropheryma whipplei pneumonia revealed by Metagenomic next-generation sequencing: Report of two cases.

Tropheryma whipplei is the causative agent of Whipple's disease, which is a rare multiorgan systemic disease. We report two cases of Tropheryma whipplei infection, all routine tests were negative and it was finally detected by mNGS. This may help clinicians increase awareness of the diagnosis and treatment of acute severe pneumonia and interstitial pneumonia caused by Tropheryma whipplei.

Metagenomic next-generation sequencing confirms the diagnosis of Legionella pneumonia with rhabdomyolysis and acute kidney injury in a limited resource area: a case report and review.

Background: Legionella pneumonia, rhabdomyolysis, and acute kidney injury are called the Legionella triad, which is rare and associated with a poor outcome and even death. Early diagnosis and timely treatment are essential for these patients. Case presentation: A 63-year-old man with cough, fever, and fatigue was initially misdiagnosed with common bacterial infection and given beta-lactam monotherapy but failed to respond to it. Conventional methods, including the first Legionella antibody test, sputum smear, and culture of sputum, blood, and bronchoalveolar lavage fluid (BALF) were negative. He was ultimately diagnosed with a severe infection of Legionella pneumophila by metagenomics next-generation sequencing (mNGS). This patient, who had multisystem involvement and manifested with the rare triad of Legionella pneumonia, rhabdomyolysis, and acute kidney injury, finally improved after combined treatment with moxifloxacin, continuous renal replacement therapy, and liver protection therapy. Conclusion: Our results showed the necessity of early diagnosis of pathogens in severe patients, especially in Legionnaires' disease, who manifested with the triad of Legionella pneumonia, rhabdomyolysis, and acute kidney injury. mNGS may be a useful tool for Legionnaires' disease in limited resource areas where urine antigen tests are not available.

Deep Learning Technology Applied to Medical Image Tissue Classification.

Medical image classification is a novel technology that presents a new challenge. It is essential that pathological images are automatically and correctly classified to enable doctors to provide precise treatment. Convolutional neural networks have demonstrated their effectiveness in classifying images in deep learning, which may have dozens or hundreds of layers, to illustrate the relationship between them in terms of their different neural network features. Convolutional layers consisting of small kernels take weights as input and guide them through an activation function as output. The main advantage of using convolutional neural networks (CNNs) instead of traditional neural networks is that they reduce the model parameters for greater accuracy. However, many studies have simply been focused on finding the best CNN model and classification results from a single medical image classification. Therefore, we applied a common deep learning network model in an attempt to identify the best model framework by training and validating different model parameters to classify medical images. After conducting experiments on six publicly available databases of pathological images, including colorectal cancer tissue, chest X-rays, common skin lesions, diabetic retinopathy, pediatric chest X-ray, and breast ultrasound image datasets, we were able to confirm that the recognition accuracy of the Inception V3 method was significantly better than that of other existing deep learning models.

Sensitivity to copy number variation analysis in single cell genomics.

While previous studies have given some guidance, the sensitivity of copy number calling in single-cell genomics is still not comprehensive. We studied the impact of sequencing depth and other factors on single-cell copy number analysis. Sequencing Data from 26 datasets were retrieved, and 2946 single cells passed the filter. Thirty-eight single cells were independently downscaled to evaluate copy number variation (CNV) detection sensitivity at different bin sizes. The sensitivity of whole genome amplification (WGA) approaches and cell types to CNV calling were evaluated using downsampling of 101 and 70 cells. Cluster analysis based on t-SNE was executed to evaluate CNV calling performance. Our results suggest 0.75× sequencing depth with moderate resolution (250 kb bin size) may be a practical guideline considering both sequencing cost and performance of copy number calling, which can be appropriately optimized based on amplification approach, cell type, and sample complexity.

Obtaining Genome Sequences of Mutualistic Bacteria in Single Microcystis Colonies.

Cells of Microcystis are associated with heterotrophic bacteria and organized in colonies in natural environment, which are basic elements in the mass occurrence of cyanobacterial species. Analyzing these colonies by using metagenomics is helpful to understand species composition and relationship. Meanwhile, the difference in population abundance among Microcystis colonies could be used to recover genome bins from metagenome assemblies. Herein, we designed a pipeline to obtain high-quality genomes of mutualistic bacteria from single natural Microcystis colonies. Single colonies were lysed, and then amplified by using multiple displacement amplification to overcome the DNA quantity limit. A two-step assembly was performed after sequencing and scaffolds were grouped into putative bins based on their differential-coverage among species. We analyzed six natural colonies of three prevailing Microcystis species from Lake Taihu. Clustering results proved that colonies of the same species were similar in the microbial community composition. Eight putative population genome bins with wide bacterial diversity and different GC content were identified based on coverage difference among colonies. At the phylum level, proteobacteria was the most abundant besides cyanobacteria. Six of the population bins were further refined into nearly complete genomes (completeness > 90%).

ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing.

As the most widely-used single cell whole genome amplification (WGA) approach, multiple displacement amplification (MDA) has a superior performance, due to the high-fidelity and processivity of phi29 DNA polymerase. However, chimeric reads, generated in MDA, cause severe disruption in many single-cell studies. Herein, we constructed ChimeraMiner, an improved chimeric read detection pipeline for analyzing the sequencing data of MDA and classified the chimeric sequences. Two datasets (MDA1 and MDA2) were used for evaluating and comparing the efficiency of ChimeraMiner and previous pipeline. Under the same hardware condition, ChimeraMiner spent only 43.4% (43.8% for MDA1 and 43.0% for MDA2) processing time. Respectively, 24.4 million (6.31%) read pairs out of 773 million reads, and 17.5 million (6.62%) read pairs out of 528 million reads were accurately classified as chimeras by ChimeraMiner. In addition to finding 83.60% (17,639,371) chimeras, which were detected by previous pipelines, ChimeraMiner screened 6,736,168 novel chimeras, most of which were missed by the previous pipeline. Applying in single-cell datasets, all three types of chimera were discovered in each dataset, which introduced plenty of false positives in structural variation (SV) detection. The identification and filtration of chimeras by ChimeraMiner removed most of the false positive SVs (83.8%). ChimeraMiner revealed improved efficiency in discovering chimeric reads, and is promising to be widely used in single-cell sequencing.

Accurate and sensitive single-cell-level detection of copy number variations by micro-channel multiple displacement amplification (µcMDA).

Whole genome amplification (WGA) has laid the foundation for investigating complex genomic alteration with single-cell or even single-molecule resolution. Coupled with sequencing-based copy number variation (CNV) analysis, it promotes understanding of the nature of commonly existing genetic heterogeneity by constructing the sequencing profiles for every single cell. However, prevailing methods only provide insights into limited aspects due to their intrinsic technical challenges. Their output data, as a result, fails to render comprehensive information (which is) concerned. Here, we describe the CNV detection analysis based on micro-channel multiple displacement amplification (µcMDA), a protocol able to provide optimized amplification uniformity while inheriting the advantages of MDA chemistry. We demonstrate the analysis of both the normal diploid YH-1 cell line and the aneuploid K562 cancer cell line. In the detection of simulated CNVs ranging from 300 kb to 2 Mb, µcMDA can respectively increase the detection rates of copy number loss and gain by 28.8% and 40.2% on average, using only 0.2× sequencing data. When detecting the inherent CNVs in tumor cells, the resolution of CNV recognition can be improved to 250 kb. Starting from either superabundant template copies or minute single-cell-level input, this easily accessible approach is capable of providing quantitatively reliable coverage as well as more robust GC-content regression for CNV detection.

1D-Reactor Decentralized MDA for Uniform and Accurate Whole Genome Amplification.

Multiple displacement amplification (MDA), a most popular isothermal whole genome amplification (WGA) method, suffers the major hurdle of highly uneven amplification, thus, leading to many problems in approaching biological applications related to copy-number assessment. In addition to the optimization of reagents and conditions, complete physical separation of the entire reaction system into numerous tiny chambers or droplets using microfluidic devices, has been proven efficient to mitigate this amplifying bias in recent works. Here, we present another MDA advance, microchannel MDA (µcMDA), which decentralizes MDA reagents throughout a one-dimensional slender tube. Due to the double effect from soft partition of high molecular-weight DNA molecules and less-limited diffusion of small particles, µcMDA is shown to be significantly effective at improving the amplification uniformity, which enables us to accurately detect single nucleotide variants (SNVs) with higher efficiency and sensitivity. More importantly, this straightforward method requires neither customized instruments nor complicated operations, making it a ready-to-use technique in almost all biological laboratories.

Effect of morroniside on glomerular mesangial cells through AGE-RAGE pathway.

Diabetes mellitus is an endocrine and metabolic disease characterized by high blood glucose. Diabetic nephropathy (DN) is one of the most typical diabetic complications. Cornus officinalis is a type of traditional Chinese medicine that replenishes the liver and kidney. Morroniside is one of the main characteristic components of C. officinalis. In this study, an in vitro model for simulating DN damage was established by stimulating rat glomerular mesangial cells by the advanced glycation end products. The protective mechanism and effect of morroniside in regulating receptor for advanced glycation end products signaling pathway in DN was investigated to provide experimental evidence for the prevention and treatment of DN.

[Protective effects of da chai hu granules (DCHKL) against alloxan (AXN)-induced rat pancreatic islets damage].

The protective effects of Da Chai Hu Granules (DCHKL) on islet cells which were incubated with 4 mmol x L(-1) alloxan (AXN) were studied. The viability of islet cells were measured with MTT. Insulin released into medium and in islets was detected by radioimmunoassay. Cell apoptosis rate was determined by flow cytometry. The expression of anti-apoptotic gene Bcl-2 and pro-apoptotic gene Bax in islet cells were measured with RT-PCR (reverse transcription polymerase chain reaction). Serum containing DCHKL can promote the activity of islet cells significantly (P < 0.01). Basal insulin secretion and high glucose-stimulated insulin secretion increased significantly (P < 0.01). Serum containing DCHKL can inhibit apoptosis of islet cells, the ratio of apoptosis was decreased. Serum containing DCHKL increased expression of Bcl-2 mRNA and decreased expression of Bax mRNA. DCHKL can significantly promote proliferation of islet cells and increase the amount of basal secretion of pancreatic islet cells and high glucose-stimulated insulin secretion. The expression of Bcl-2 increased significantly. The expression of Bax decreased significantly. DCHKL have a protective effect on the islet cells.