GRable Version 1.0: A Software Tool for Site-Specific Glycoform Analysis With Improved MS1-Based Glycopeptide Detection With Parallel Clustering and Confidence Evaluation With MS2 Information.

High-throughput intact glycopeptide analysis is crucial for elucidating the physiological and pathological status of the glycans attached to each glycoprotein. Mass spectrometry-based glycoproteomic methods are challenging because of the diversity and heterogeneity of glycan structures. Therefore, we developed an MS1-based site-specific glycoform analysis method named "Glycan heterogeneity-based Relational IDentification of Glycopeptide signals on Elution profile (Glyco-RIDGE)" for a more comprehensive analysis. This method detects glycopeptide signals as a cluster based on the mass and chromatographic properties of glycopeptides and then searches for each combination of core peptides and glycan compositions by matching their mass and retention time differences. Here, we developed a novel browser-based software named GRable for semi-automated Glyco-RIDGE analysis with significant improvements in glycopeptide detection algorithms, including "parallel clustering." This unique function improved the comprehensiveness of glycopeptide detection and allowed the analysis to focus on specific glycan structures, such as pauci-mannose. The other notable improvement is evaluating the "confidence level" of the GRable results, especially using MS2 information. This function facilitated reduced misassignment of the core peptide and glycan composition and improved the interpretation of the results. Additional improved points of the algorithms are "correction function" for accurate monoisotopic peak picking; one-to-one correspondence of clusters and core peptides even for multiply sialylated glycopeptides; and "inter-cluster analysis" function for understanding the reason for detected but unmatched clusters. The significance of these improvements was demonstrated using purified and crude glycoprotein samples, showing that GRable allowed site-specific glycoform analysis of intact sialylated glycoproteins on a large-scale and in-depth. Therefore, this software will help us analyze the status and changes in glycans to obtain biological and clinical insights into protein glycosylation by complementing the comprehensiveness of MS2-based glycoproteomics. GRable can be freely run online using a web browser via the GlyCosmos Portal (https://glycosmos.org/grable).

Rapid Quantification of Microvessels of Three-Dimensional Blood-Brain Barrier Model Using Optical Coherence Tomography and Deep Learning Algorithm.

The blood-brain barrier (BBB) is a selective barrier that controls the transport between the blood and neural tissue features and maintains brain homeostasis to protect the central nervous system (CNS). In vitro models can be useful to understand the role of the BBB in disease and assess the effects of drug delivery. Recently, we reported a 3D BBB model with perfusable microvasculature in a Transwell insert. It replicates several key features of the native BBB, as it showed size-selective permeability of different molecular weights of dextran, activity of the P-glycoprotein efflux pump, and functionality of receptor-mediated transcytosis (RMT), which is the most investigated pathway for the transportation of macromolecules through endothelial cells of the BBB. For quality control and permeability evaluation in commercial use, visualization and quantification of the 3D vascular lumen structures is absolutely crucial. Here, for the first time, we report a rapid, non-invasive optical coherence tomography (OCT)-based approach to quantify the microvessel network in the 3D in vitro BBB model. Briefly, we successfully obtained the 3D OCT images of the BBB model and further processed the images using three strategies: morphological imaging processing (MIP), random forest machine learning using the Trainable Weka Segmentation plugin (RF-TWS), and deep learning using pix2pix cGAN. The performance of these methods was evaluated by comparing their output images with manually selected ground truth images. It suggested that deep learning performed well on object identification of OCT images and its computation results of vessel counts and surface areas were close to the ground truth results. This study not only facilitates the permeability evaluation of the BBB model but also offers a rapid, non-invasive observational and quantitative approach for the increasing number of other 3D in vitro models.

Cortical transcriptome analysis after spinal cord injury reveals the regenerative mechanism of central nervous system in CRMP2 knock-in mice.

Recent studies have shown that mutation at Ser522 causes inhibition of collapsin response mediator protein 2 (CRMP2) phosphorylation and induces axon elongation and partial recovery of the lost sensorimotor function after spinal cord injury (SCI). We aimed to reveal the intracellular mechanism in axotomized neurons in the CRMP2 knock-in (CRMP2KI) mouse model by performing transcriptome analysis in mouse sensorimotor cortex using micro-dissection punching system. Prior to that, we analyzed the structural pathophysiology in axotomized or neighboring neurons after SCI and found that somatic atrophy and dendritic spine reduction in sensorimotor cortex were suppressed in CRMP2KI mice. Further analysis of the transcriptome has aided in the identification of four hemoglobin genes Hba-a1, Hba-a2, Hbb-bs, and Hbb-bt that are significantly upregulated in wild-type mice with concomitant upregulation of genes involved in the oxidative phosphorylation and ribosomal pathways after SCI. However, we observed substantial upregulation in channel activity genes and downregulation of genes regulating vesicles, synaptic function, glial cell differentiation in CRMP2KI mice. Moreover, the transcriptome profile of CRMP2KI mice has been discussed wherein energy metabolism and neuronal pathways were found to be differentially regulated. Our results showed that CRMP2KI mice displayed improved SCI pathophysiology not only via microtubule stabilization in neurons, but also possibly via the whole metabolic system in the central nervous system, response changes in glial cells, and synapses. Taken together, we reveal new insights on SCI pathophysiology and the regenerative mechanism of central nervous system by the inhibition of CRMP2 phosphorylation at Ser522. All these experiments were performed in accordance with the guidelines of the Institutional Animal Care and Use Committee at Waseda University, Japan (2017-A027 approved on March 21, 2017; 2018-A003 approved on March 25, 2018; 2019-A026 approved on March 25, 2019).

Comparison of two screening tests for HIV-Associated Neurocognitive Disorder suspected Japanese patients with respect to cART usage.

In this study, we demonstrated the pervasiveness of HIV-associated neurocognitive disorders (HAND) among a selection of Japanese patients as well as evaluated and compared the Mini Mental State Examination (MMSE) and the International HIV Dementia Scale (IHDS) for use as a screening tool among combination anti-retroviral therapy (cART)-naïve and cART experienced patients. The MMSE and the IHDS have both been used as HAND screening tests around the world with variable success. It has been reported the increased usage of cART the utility of these screening tests may have been diminished due to the decreased severity of impairment and the altered pattern of neurocognitive impairments in cART era HAND patients. It is therefore possible the MMSE and the IHDS may still be useful among cART-naïve patients even in the cART era. However, only one study has investigated and compared the screening results of the IHDS among cART-naïve and cART experienced patients. All HIV positive patients who visited, or were admitted, to the Ryukyu University Hospital between January 2009 and March 2014 were evaluated for inclusion. Selected patients (n = 49) had data without omission for all tests. The overall prevalence of HAND in our cohort was 44%. The area under the curve (AUC), for all subjects using the MMSE and the IHDS, were 0.60 and 0.69, respectively. However, the AUC among cART-naïve patients were 0.58 and 0.76 for the MMSE and the IHDS, respectively. Whereas, cART experienced patients had an AUC of 0.60 and 0.61, respectively. Overall, the MMSE demonstrated a poor screening ability for HAND, regardless of cART usage (the cut-off value of 27 had a Youden's J-Index of 0.1, in all groups). Alternatively, the IHDS was moderately useful for HAND screening among cART-naïve patients (the cut-off value of 11 had a Youden's J-Index of 0.4), but performed poorly as a screening test among cART experienced patients (the cut-off value of 11 had a Youden's J-Index of 0.1).

Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways.

Measuring the concentrations of metabolites and estimating the reaction rates of each reaction step consisting of metabolic pathways are significant for an improvement in microorganisms used in maximizing the production of materials. Although the reaction pathway must be identified for such an improvement, doing so is not easy. Numerous reaction steps have been reported; however, the actual reaction steps activated vary or change according to the conditions. Furthermore, to build mathematical models for a dynamical analysis, the reaction mechanisms and parameter values must be known; however, to date, sufficient information has yet to be published for many cases. In addition, experimental observations are expensive. A new mathematical approach that is applicable to small sample data, and that requires no detailed reaction information, is strongly needed. S-system is one such model that can use smaller samples than other ordinary differential equation models. We propose a simplified S-system to apply minimal quantities of samples for a dynamic analysis of the metabolic pathways. We applied the model to the phenyl lactate production pathway of Escherichia coli. The model obtained suggests that actually activated reaction steps and feedback are inhibitions within the pathway.

Factors Leading to Improved Gait Function in Patients with Subacute or Chronic Central Nervous System Impairments Who Receive Functional Training with the Robot Suit Hybrid Assistive Limb.

The factors that lead to the improvement of gait function in patients with diseases of the central nervous system (CNS) who use a hybrid assistive limb (HAL) are not yet fully understood. The purpose of the present study was to analyze these factors to determine the prognosis of the patients' gait function. Patients whose CNS disease was within 180 days since onset were designated as the subacute-phase patients, and patients whose disease onset had occurred more than 180 days previously were designated as chronic-phase patients. Fifteen subacute-phase patients and 15 chronic-phase patients were given HAL training. The study analyzed how post-training walking independence in these patients was affected by the following factors: age, disease, lesion area, lower limb function, balance, period until the start of training, number of training sessions, additional rehabilitation, higher-order cognitive dysfunction, HAL model, and the use of a non-weight-bearing walking-aid. In subacute-phase patients, walking independence was related to lower limb function (rs = 0.35). In chronic-phase patients, there was a statistically significant correlation between post-training walking independence and balance (rs = 0.78). In addition, in patients with a severe motor dysfunction that was accompanied by inattention and global cognitive dysfunction, little improvement occurred, even with double-leg model training, because they had difficulty wearing the device. The results demonstrated that the factors that improved walking independence post HAL training differed between patients with subacute- and chronic-stage CNS diseases. The findings may serve as valuable information for future HAL training of patients with CNS diseases.

Cerebellar Contribution to Pattern Separation of Human Hippocampal Memory Circuits.

The cerebellum is a crucial structure for cognitive function as well as motor control. Benign brain tumors such as schwannomas, meningiomas, and epidermoids tend to occur in the cerebellopontine angle cisterns and may cause compression of the posterior lateral cerebellum near the superior posterior fissure, where the eloquent area for cognitive function was recently identified. The present study examined cognitive impairment in patients with benign cerebellar tumors before and after surgical intervention in order to clarify the functional implications of this region in humans. Patients with cerebellar tumors showed deficits in psychomotor speed and working memory compared with healthy controls. Moreover, these impairments were more pronounced in patients with right cerebellar tumors. Functional magnetic resonance imaging during performance of a lure task also demonstrated that cerebellar tumors affected pattern separation or the ability to distinguish similar experiences of episodic memory or events with discrete, non-overlapping representations, which is one of the important cognitive functions related to the hippocampus. The present findings indicate that compression of the human posterior lateral cerebellum affects hippocampal memory function.

Three-component domino process for the pyrrolizine skeleton via [3 + 2]-cycloaddition-enamine cyclization triggered by a gold catalyst.

Pyrrolizines are bicyclic fused azaheterocycles with a bridgehead nitrogen contained in a core skeleton and are often found in biologically active compounds. Despite their importance, there have been few reports on concise and flexible syntheses of pyrrolizines. A novel one-pot, convergent method is described for pyrrolizines by simple mixing of iminoesters, acetylenes, and dipolarophiles in the presence of a cationic gold catalyst and an acid additive. This domino process affords multisubstituted pyrrolizines without handling unstable intermediates.

Analysis of pre- and post-operative symptoms of patients with mild trigonocephaly using several developmental and psychological tests.

PURPOSE: Over the past decade, we collected the cases where patients underwent decompressive cranioplasty for the treatment of mild metopic suture synostosis (mild trigonocephaly) with developmental delays. To evaluate the effectiveness of this surgery, we administered several developmental and psychological examinations to children with this condition who underwent decompressive cranioplasty. METHODS: Thirty-four children (32 boys and 2 girls) who had developmental disorders with mild trigonocephaly underwent four different tests at three different time points (pre-operation, 3 and 6 months after surgery) including the: (a) Kyoto form developmental test (2001) to calculate the developmental quotient (DQ), (b) National Rehabilitation Center Sign-Significance Test (NRC S-S test) to evaluate the patients' language use and acquisition, (c) Pervasive Developmental Disorders Autism Society Japan Rating Scale (PARS) to identify autistic tendencies, and (d) Japanese Child Behavior Checklist (J-CBCL) to evaluate behavioral problems. The scores were initially analyzed using analyses of variance. When significant results were observed, Tukey-Kramer multiple comparison tests were applied for further statistical evaluation. RESULTS: Significant DQ improvements were observed, as assessed by the Kyoto form developmental test. Additionally, significant improvement in the expression of words (measured with the NRC S-S test), the scores on PARS, and some behavioral factors (measured with the J-CBCL) were observed. CONCLUSIONS: The results in this cohort suggest that decompressive cranioplasty may play an important role in supporting the improvement of developmental delays in these patients.

Statistical stage transition detection method for small sample gene expression time series data.

In terms of their internal (genetic) and external (phenotypic) states, living cells are always changing at varying rates. Periods of stable or low rate of change are often called States, Stages, or Phases, whereas high-rate periods are called Transitions or Transients. While states and transitions are observed phenotypically, such as cell differentiation, cancer progression, for example, are related with gene expression levels. On the other hand, stages of gene expression are definable based on changes of expression levels. Analyzing relations between state changes of phenotypes and stage transitions of gene expression levels is a general approach to elucidate mechanisms of life phenomena. Herein, we propose an algorithm to detect stage transitions in a time series of expression levels of a gene by defining statistically optimal division points. The algorithm shows detecting ability for simulated datasets. An annotation based analysis on detecting results for a dataset of initial development of Caenorhabditis elegans agrees with that are presented in the literature.

Are MMSE and HDS-R neuropsychological tests adequate for screening HIV-associated neurocognitive disorders?

HIV-associated neurocognitive disorders (HAND) are one of major comorbidities in patients with HIV-1 infection. There are currently no standardized tests for screening HAND in such patients. The sensitivity of the cognitive function tests routinely used in clinical practice, such as the Mini-Mental State Examination and the Revised Hasegawa's Dementia Scale, is inadequate to rule out HAND, even in patients with clear abnormal behavior. We report a 41-year-old man with HIV-associated dementia, the most severe form of HAND, in whom the simplified methods did not show abnormal results, and a comprehensive battery of neuropsychological tests which covering several cognitive domains was needed to detect cognitive impairment.

MFSPSSMpred: identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation.

BACKGROUND: Molecular recognition features (MoRFs) are short binding regions located in longer intrinsically disordered protein regions. Although these short regions lack a stable structure in the natural state, they readily undergo disorder-to-order transitions upon binding to their partner molecules. MoRFs play critical roles in the molecular interaction network of a cell, and are associated with many human genetic diseases. Therefore, identification of MoRFs is an important step in understanding functional aspects of these proteins and in finding applications in drug design. RESULTS: Here, we propose a novel method for identifying MoRFs, named as MFSPSSMpred (Masked, Filtered and Smoothed Position-Specific Scoring Matrix-based Predictor). Firstly, a masking method is used to calculate the average local conservation scores of residues within a masking-window length in the position-specific scoring matrix (PSSM). Then, the scores below the average are filtered out. Finally, a smoothing method is used to incorporate the features of flanking regions for each residue to prepare the feature sets for prediction. Our method employs no predicted results from other classifiers as input, i.e., all features used in this method are extracted from the PSSM of sequence only. Experimental results show that, comparing with other methods tested on the same datasets, our method achieves the best performance: achieving 0.004~0.079 higher AUC than other methods when tested on TEST419, and achieving 0.045~0.212 higher AUC than other methods when tested on TEST2012. In addition, when tested on an independent membrane proteins-related dataset, MFSPSSMpred significantly outperformed the existing predictor MoRFpred. CONCLUSIONS: This study suggests that: 1) amino acid composition and physicochemical properties in the flanking regions of MoRFs are very different from those in the general non-MoRF regions; 2) MoRFs contain both highly conserved residues and highly variable residues and, on the whole, are highly locally conserved; and 3) combining contextual information with local conservation information of residues facilitates the prediction of MoRFs.

Estimate hidden dynamic profiles of siRNA effect on apoptosis.

BACKGROUND: For the representation of RNA interference (RNAi) dynamics, several mathematical models based on systems of ordinary differential equations (ODEs) have been proposed. These models consist of equations for each molecule that are involved in RNAi phenomena. Therefore, many real-value parameters must be optimized to identify the models. They also have many 'hidden variables', which cannot be observed directly through experimentation. Calculation of the values of the hidden variables is generally very difficult, if not impossible in some special cases. Identification of the ODE models is also quite difficult. RESULTS: We show that the simplified logistic Lotka-Volterra model, a well-established ODE model for biological and biochemical phenomena, can represent RNAi dynamics as a predator-prey system. Although a hidden variable exists in the model, its values can be determined and made visible as dynamic profiles of RNA-decomposing effects of siRNAs. Correlation analysis shows that the model parameters correlate highly with the total effect of the siRNA. CONCLUSIONS: The results suggest that analyses using our model are useful to estimate dynamic profiles of siRNA effects on apoptosis and to score siRNA by its effects on apoptosis, namely 'phenotypic scoring'.

Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset.

BACKGROUND: Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. RESULTS: As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. CONCLUSIONS: The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions.

Periodicity detection method for small-sample time series datasets.

Time series of gene expression often exhibit periodic behavior under the influence of multiple signal pathways, and are represented by a model that incorporates multiple harmonics and noise. Most of these data, which are observed using DNA microarrays, consist of few sampling points in time, but most periodicity detection methods require a relatively large number of sampling points. We have previously developed a detection algorithm based on the discrete Fourier transform and Akaike's information criterion. Here we demonstrate the performance of the algorithm for small-sample time series data through a comparison with conventional and newly proposed periodicity detection methods based on a statistical analysis of the power of harmonics.We show that this method has higher sensitivity for data consisting of multiple harmonics, and is more robust against noise than other methods. Although "combinatorial explosion" occurs for large datasets, the computational time is not a problem for small-sample datasets. The MATLAB/GNU Octave script of the algorithm is available on the author's web site: http://www.cbrc.jp/%7Etominaga/piccolo/.

Judgment algorithm for periodicity of time series data based on Bayesian information criterion.

Judgment periodicity of biological time series data is important and done widely to find the circadian expression of genes, monthly change of hormones, etc. To keep complete reproducibility of judgment is a problem because popular judgment methods such as curve fitting, Fourier analysis, etc. need judgment criteria determined by analysts considering experimental conditions and results (level, S/N, distribution, etc.) based on their experience. Judgment results are often affected by analysts' subjects. Reproducible criterion determination is therefore strongly needed. We propose introducing the information criterion to replace analysts' criteria. A judgment algorithm by combining Bayesian information criterion (BIC) and discrete Fourier transform (DFT) has been developed and has proved its ability through application to mice microarray data and finding of circadian genes. Our method, named "Piccolo", shows higher sensitivity than the simple DFT (without BIC) method with reproducibility, and can be fully automated.

Cognitive function of patients with brain tumor in pre- and postoperative stage.

BACKGROUND: Nobody knows whether cognitive dysfunction affects survival. Furthermore, it is unknown whether the dysfunction is caused by the tumor itself or by its treatment. METHODS: Patients with 20 gliomas (LGG, 7; MG, 13 [AG, 4; GM, 9]) in the right brain (nondominant) and 11 gliomas (LGG, 1; MG, 10 [AG, 6; GM, 4]) in the left brain (dominant) were studied. Thirty-four patients with meningioma were also studied. Cognitive function was evaluated by the 3MS examination, and propriety of radical resection of tumor was reviewed. RESULTS: Cognitive function pre-Op and post-Op was normal in patients with LGG and MGs in the right brain but decreased before an Op in all patients with MG in the left brain, and they did not normalize after Op. In patients with MG in left brain, the test of temporal and spatial orientation, first recall, similarities, 4-legged animals, mental reversal, and writing decreased after Op. Cognitive hypofunction before or after Op did not correlate with tumor malignancy and degree of tumor resection. CONCLUSION: Firstly, radical Op should aim at improvement of cognitive function for patients with glioma in the right brain, and for patients with glioma in the left brain, QOL should be thought about without expecting improvement of cognitive function. Secondly, improvement of cognitive function cannot be anticipated in patients with meningioma in the left brain. Aged patients older than 75 years require carefulness in Op. Then, damage of the cingulated gyrus and corpus callosum should be avoided in the left brain. This study emphasizes that clinicians should be careful in the evaluation of cognitive function in glioma and meningioma treatment.

Dynamic modeling of genetic networks using genetic algorithm and S-system.

MOTIVATION: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. RESULTS: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.