More is different: Reconstituting complexity in microtubule regulation.

Microtubules are dynamic cytoskeletal filaments that undergo stochastic switching between phases of polymerization and depolymerization-a behavior known as dynamic instability. Many important cellular processes, including cell motility, chromosome segregation, and intracellular transport, require complex spatiotemporal regulation of microtubule dynamics. This coordinated regulation is achieved through the interactions of numerous microtubule-associated proteins (MAPs) with microtubule ends and lattices. Here, we review the recent advances in our understanding of microtubule regulation, focusing on results arising from biochemical in vitro reconstitution approaches using purified multiprotein ensembles. We discuss how the combinatory effects of MAPs affect both the dynamics of individual microtubule ends, as well as the stability and turnover of the microtubule lattice. In addition, we highlight new results demonstrating the roles of protein condensates in microtubule regulation. Our overall intent is to showcase how lessons learned from reconstitution approaches help unravel the regulatory mechanisms at play in complex cellular environments.

Microtubule search-and-capture model evaluates the effect of chromosomal volume conservation on spindle assembly during mitosis.

Variation in the chromosome numbers can arise from the erroneous mitosis or fusion and fission of chromosomes. While the mitotic errors lead to an increase or decrease in the overall chromosomal substance in the daughter cells, fission and fusion keep this conserved. Variations in chromosome numbers are assumed to be a crucial driver of speciation. For example, the members of the muntjac species are known to have very different karyotypes with the chromosome numbers varying from 2n=70+3B in the brown brocket deer to 2n=46 in the Chinese muntjac and 2n=6/7 in the Indian muntjac. The chromosomal content in the nucleus of these closely related mammals is roughly the same and various chromosome fusion and fission pathways have been suggested as the evolution process of these karyotypes. Similar trends can also be found in lepidoptera and yeast species which show a wide variation of chromosome numbers. The effect of chromosome number variation on the spindle assembly time and accuracy is still not properly addressed. We computationally investigate the effect of conservation of the total chromosomal substance on the spindle assembly during prometaphase. Our results suggest that chromosomal fusion pathways aid the microtubule-driven search and capture of the kinetochore in cells with monocentric chromosomes. We further report a comparative analysis of the site and percentage of amphitelic captures, dependence on cell shape, and position of the kinetochore in respect to chromosomal volume partitioning.

CLASPs stabilize the pre-catastrophe intermediate state between microtubule growth and shrinkage.

Cytoplasmic linker-associated proteins (CLASPs) regulate microtubules in fundamental cellular processes. CLASPs stabilize dynamic microtubules by suppressing microtubule catastrophe and promoting rescue, the switch-like transitions between growth and shrinkage. How CLASPs specifically modulate microtubule transitions is not understood. Here, we investigate the effects of CLASPs on the pre-catastrophe intermediate state of microtubule dynamics, employing distinct microtubule substrates to mimic the intermediate state. Surprisingly, we find that CLASP1 promotes the depolymerization of stabilized microtubules in the presence of GTP, but not in the absence of nucleotide. This activity is also observed for CLASP2 family members and a minimal TOG2-domain construct. Conversely, we find that CLASP1 stabilizes unstable microtubules upon tubulin dilution in the presence of GTP. Strikingly, our results reveal that CLASP1 drives microtubule substrates with vastly different inherent stabilities into the same slowly depolymerizing state in a nucleotide-dependent manner. We interpret this state as the pre-catastrophe intermediate state. Therefore, we conclude that CLASPs suppress microtubule catastrophe by stabilizing the intermediate state between growth and shrinkage.

Evaluation of dose-dependent treatment effects after mid-trial dose escalation in biomarker, clinical, and cognitive outcomes for gantenerumab or solanezumab in dominantly inherited Alzheimer's disease.

Introduction: While the Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU) was ongoing, external data suggested higher doses were needed to achieve targeted effects; therefore, doses of gantenerumab were increased 5-fold, and solanezumab was increased 4-fold. We evaluated to what extent mid-trial dose increases produced a dose-dependent treatment effect. Methods: Using generalized linear mixed effects (LME) models, we estimated the annual low- and high-dose treatment effects in clinical, cognitive, and biomarker outcomes. Results: Both gantenerumab and solanezumab demonstrated dose-dependent treatment effects (significant for gantenerumab, non-significant for solanezumab) in their respective target amyloid biomarkers (Pittsburgh compound B positron emission tomography standardized uptake value ratio and cerebrospinal fluid amyloid beta 42), with gantenerumab demonstrating additional treatment effects in some downstream biomarkers. No dose-dependent treatment effects were observed in clinical or cognitive outcomes. Conclusions: Mid-trial dose escalation can be implemented as a remedy for an insufficient initial dose and can be more cost effective and less burdensome to participants than starting a new trial with higher doses, especially in rare diseases. Highlights: We evaluated the dose-dependent treatment effect of two different amyloid-specific immunotherapies.Dose-dependent treatment effects were observed in some biomarkers.No dose-dependent treatment effects were observed in clinical/cognitive outcomes, potentially due to the fact that the modified study may not have been powered to detect such treatment effects in symptomatic subjects at a mild stage of disease exposed to high (or maximal) doses of medication for prolonged durations.

Predictive signature development based on maximizing the area between receiver operating characteristic curves.

Development of marker signatures to predict treatment benefits for a new therapeutic is an important scientific component in advancing the drug discovery and is an important first step toward the goal of precision medicine. In this article, we focus on developing an algorithm to search for optimal linear combination of markers that maximizes the area between two receiver operating characteristic curves of the new therapeutic and the control groups without assuming any parametric model. We further generalize the proposed algorithm for predictive signature development to maximize the difference of Harrel's C-index of the new therapeutic and the control groups when the outcome of interest is time-to-event. The performance of this proposed method is evaluated and compared to existing methods via simulations and real clinical trial data.

Differential expression of single-cell RNA-seq data using Tweedie models.

The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse.

Mechanics of microtubule organizing center clustering and spindle positioning in budding yeast Cryptococcus neoformans.

The dynamic process of mitotic spindle assembly depends on multitudes of inter-dependent interactions involving kinetochores (KTs), microtubules (MTs), spindle pole bodies (SPBs), and molecular motors. Before forming the mitotic spindle, multiple visible microtubule organizing centers (MTOCs) coalesce into a single focus to serve as an SPB in the pathogenic budding yeast, Cryptococcus neoformans. To explain this unusual phenomenon in the fungal kingdom, we propose a "search and capture" model, in which cytoplasmic MTs (cMTs) nucleated by MTOCs grow and capture each other to promote MTOC clustering. Our quantitative modeling identifies multiple redundant mechanisms mediated by a combination of cMT-cell cortex interactions and inter-cMT coupling to facilitate MTOC clustering within the physiological time limit as determined by time-lapse live-cell microscopy. Besides, we screen various possible mechanisms by computational modeling and propose optimal conditions that favor proper spindle positioning-a critical determinant for timely chromosome segregation. These analyses also reveal that a combined effect of MT buckling, dynein pull, and cortical push maintains spatiotemporal spindle localization.

Remdesivir: Critical Clinical Appraisal for COVID 19 Treatment.

Remdesivir is presently been considered as 'molecule of hope' to curb the menace of COVID19. Non-availability of any USFDA approved drug has led to several attempt of drug-repurposing and development of new therapeutic molecules. However, Remdesivir has been found to be effective against a broad range of virus including SARS, MERS and COVID 19 through in-vitro studies. Several clinical research attempt are presently being conducted showing promising result yet not conclusive. This review summarized all such clinical trials to critically appraise the usage of Remdesivir against COVID 19 along with the publications related to the results of the clinical studies. The present regulatory aspect i. e. Emergency Use Authorization (EYA) and information of molecule and plausible mechanism is also dealt.

Studying the progress of COVID-19 outbreak in India using SIRD model.

We explore a standard epidemiological model, known as the SIRD model, to study the COVID-19 infection in India, and a few other countries around the world. We use (a) the stable cumulative infection of various countries and (b) the number of infection versus the tests carried out to evaluate the model. The time-dependent infection rate is set in the model to obtain the best fit with the available data. The model is simulated aiming to project the probable features of the infection in India, various Indian states, and other countries. India imposed an early lockdown to contain the infection that can be treated by its healthcare system. We find that with the current infection rate and containment measures, the total active infection in India would be maximum at the end of June or beginning of July 2020. With proper containment measures in the infected zones and social distancing, the infection is expected to fall considerably from August. If the containment measures are relaxed before the arrival of the peak infection, more people from the susceptible population will fall sick as the infection is expected to see a threefold rise at the peak. If the relaxation is given a month after the peak infection, a second peak with a moderate infection will follow. However, a gradual relaxation of the lockdown started well ahead of the peak infection, leads to a nearly twofold increase of the peak infection with no second peak. The model is further extended to incorporate the infection arising from the population showing no symptoms. The preliminary finding suggests that random testing needs to be carried out within the asymptomatic population to contain the spread of the disease. Our model provides a semi-quantitative overview of the progression of COVID-19 in India, with model projections reasonably replicating the current progress. The projection of the model is highly sensitive to the choice of the parameters and the available data.

SEIRD model to study the asymptomatic growth during COVID-19 pandemic in India.

According to the current perception, symptomatic, presymptomatic and asymptomatic infectious persons can infect the healthy population susceptible to the SARS-CoV-2. More importantly, various reports indicate that the number of asymptomatic cases can be several-fold higher than the reported symptomatic cases. In this article, we take the reported cases in India and various states within the country till September 1, as the specimen to understand the progression of the COVID-19. Employing a modified SEIRD model, we predict the spread of COVID-19 by the symptomatic as well as asymptomatic infectious population. Considering reported infection primarily due to symptomatic, we compare the model predicted results with the available data to estimate the dynamics of the asymptomatically infected population. Our data indicate that in the absence of the asymptomatic infectious population, the number of symptomatic cases would have been much less. Therefore, the current progress of the symptomatic infection can be reduced by quarantining the asymptomatically infectious population via extensive or random testing. This study is motivated strictly toward academic pursuit; this theoretical investigation is not meant for influencing policy decisions or public health practices.

A Model-free Approach for Testing Association.

The question of association between outcome and feature is generally framed in the context of a model based on functional and distributional forms. Our motivating application is that of identifying serum biomarkers of angiogenesis, energy metabolism, apoptosis, and inflammation, predictive of recurrence after lung resection in node-negative non-small cell lung cancer patients with tumor stage T2a or less. We propose an omnibus approach for testing association that is free of assumptions on functional forms and distributions and can be used as a general method. This proposed maximal permutation test is based on the idea of thresholding, is readily implementable and is computationally efficient. We demonstrate that the proposed omnibus tests maintain their levels and have strong power for detecting linear, nonlinear and quantile-based associations, even with outlier-prone and heavy-tailed error distributions and under nonparametric setting. We additionally illustrate the use of this approach in model-free feature screening and further examine the level and power of these tests for binary outcome. We compare the performance of the proposed omnibus tests with comparator methods in our motivating application to identify preoperative serum biomarkers associated with non-small cell lung cancer recurrence in early stage patients.

An adaptive morphology based segmentation technique for lung nodule detection in thoracic CT image.

Lung cancer is one of the most life-threatening cancers mostly indicated by the presence of nodules in the lung. Doctors and radiological experts use High-Resolution Computed Tomography (HRCT) images for nodule detection and further decision making from visual inspection. Manual detection of lung nodules is a time-consuming process. Therefore, Computer-aided detection (CADe) systems have been developed for accurate nodule detection and segmentation. CADe-based systems assist radiologists to detect lung nodules with greater confidence and a lesser amount of time and have a significant impact on the accurate, uniform, and early-stage diagnosis of lung cancer. In this research work, an adaptive morphology-based segmentation technique (AMST) has been introduced by designing an adaptive morphological filter for improved segmentation of the lung nodule region. The adaptive morphological filter detects candidate nodule regions by employing adaptive structuring element (ASE) and at the same time improves nodule detection accuracy by reducing false positives (FPs) from the Computed Tomography (CT) slices. The detected nodule candidate regions are then processed for feature extraction. In this study, morphological, texture and intensity-based features have been used with support vector machine (SVM) classifier for lung nodule detection. The performance of the proposed framework has been evaluated by incorporating a 10-fold cross-validation technique on Lung Image Database Consortium-Image Database Resource Initiative (LIDC/IDRI) dataset and on a private dataset, collected from a consultant radiologist. It has been observed that the proposed automated computer-aided detection system has achieved overall classification performance indices with 94.88% sensitivity, 93.45% specificity and 94.27% detection accuracy with 1.8 FPs/scan on LIDC/IDRI dataset and 91.43% sensitivity, 90.45% specificity, 92.83% accuracy with 3.2 FPs/scan on a private dataset. The results show that the proposed CADe system presented in this paper outperforms the other state-of-the-art methods for automatic nodule detection from the HRCT image.

Mechanics of Multicentrosomal Clustering in Bipolar Mitotic Spindles.

To segregate chromosomes in mitosis, cells assemble a mitotic spindle, a molecular machine with centrosomes at two opposing cell poles and chromosomes at the equator. Microtubules and molecular motors connect the poles to kinetochores, specialized protein assemblies on the centromere regions of the chromosomes. Bipolarity of the spindle is crucial for the proper cell division, and two centrosomes in animal cells naturally become two spindle poles. Cancer cells are often multicentrosomal, yet they are able to assemble bipolar spindles by clustering centrosomes into two spindle poles. Mechanisms of this clustering are debated. In this study, we computationally screen effective forces between 1) centrosomes, 2) centrosomes and kinetochores, 3) centrosomes and chromosome arms, and 4) centrosomes and cell cortex to understand mechanics that determines three-dimensional spindle architecture. To do this, we use the stochastic Monte Carlo search for stable mechanical equilibria in the effective energy landscape of the spindle. We find that the following conditions have to be met to robustly assemble the bipolar spindle in a multicentrosomal cell: 1) the strengths of centrosomes' attraction to each other and to the cell cortex have to be proportional to each other and 2) the strengths of centrosomes' attraction to kinetochores and repulsion from the chromosome arms have to be proportional to each other. We also find that three other spindle configurations emerge if these conditions are not met: 1) collapsed, 2) monopolar, and 3) multipolar spindles, and the computational screen reveals mechanical conditions for these abnormal spindles.

A trans-dichloridoplatinum(II) complex of a monodentate nitrogen mustard: Synthesis, stability and cytotoxicity studies.

A trans-dichloridoplatinum(II) complex, trans-[PtIICl2(L)(DMSO)] (1) of a monodentate nitrogen mustard, bis(2-chloroethyl)amine (L), was synthesized by the reaction of cis-[PtIICl2(DMSO)2] &L.HCl in presence of Et3N. 1 was characterised by NMR, FT-IR and elemental analysis. L is unstable in aqueous solution while 1 displayed moderate stability. In aqueous buffer solution of pD 7.4, 1 starts to loose L slowly upon dissolution and even after 48 h there is still intact/aquated complex present in solution. 1 interacts with the model nucleobase 9-ethyl guanine. The ligand L was non-toxic against MCF-7, A549, HepG2 & MIA PaCa-2 up to 200 µM. In contrast, the Pt(II) complex 1 showed an excellent IC50 (ca. 600 nM) against MIA PaCa-2 and also displayed good IC50 value (3-7 µM) against the other cancer cell lines probed. The in vitro cytotoxicity of 1 is better than cisplatin against each of the treated cancer cell lines and it is not affected by hypoxia as per the in vitro studies. Complex 1 displays higher cellular accumulation than cisplatin and arrests the cell cycle in both S & G2/M phase inducing apoptotic cell death. The G2/M phase arrest is dominant at higher concentrations. The depolarisation of mitochondria by 1 combined with activation of caspase-7 indicates apoptotic cell death. Complex 1 induces low hemolysis of human blood signifying excellent blood compatibility.

Integration of morphological preprocessing and fractal based feature extraction with recursive feature elimination for skin lesion types classification.

BACKGROUND AND OBJECTIVE: Skin cancer is the commonest form of cancer in the worldwide population. Non-invasive and non-contact imaging modalities are being used for the screening of melanoma and other cutaneous malignancies to endorse early detection and prevention of the disease. Traditionally it has been a problem for medical personnel to differentiate melanoma, dysplastic nevi and basal cell carcinoma (BCC) diseases from one another due to the confusing appearance and similarity in the characteristics of the pigmented lesions. The paper reports an integrated method developed for identifying these skin diseases from the dermoscopic images. METHODS: The proposed integrated computer-aided method has been employed for the identification of each of these diseases using recursive feature elimination (RFE) based layered structured multiclass image classification technique. Prior to the classification, different quantitative features have been extracted by analyzing the shape, the border irregularity, the texture and the color of the skin lesions, using different image processing tools. Primarily, a combination of gray level co-occurrence matrix (GLCM) and a proposed fractal-based regional texture analysis (FRTA) algorithm has been used for the quantification of textural information. The performance of the framework has been evaluated using a layered structure classification model using support vector machine (SVM) classifier with radial basis function (RBF). RESULTS: The performance of the morphological skin lesion segmentation algorithm has been evaluated by estimating the pixel level sensitivity (Sen) of 0.9172, 0.9788 specificity (Spec), 0.9521 accuracy (ACU), along with the image similarity measuring indices as Jaccard similarity index (JSI) of 0.8562 and Dice similarity coefficient (DSC) of 0.9142 with respect to the corresponding ground truth (GT) images. The quantitative features extracted from the proposed feature extraction algorithms have been employed for the proposed multi-class skin disease identification. The proposed layered structure identifies all the three classes of skin diseases with a highly acceptable classification accuracy of 98.99%, 97.54% and 99.65% for melanoma, dysplastic nevi and BCC respectively. CONCLUSION: To overcome the difficulties of proper diagnosis of diseases based on visual evaluation, the proposed integrated system plays an important role by quantifying the effective features and identifying the diseases with higher degree of accuracy. This combined approach of quantitative and qualitative analysis not only increases the diagnostic accuracy, but also provides some important information not obtainable from qualitative assessment alone.

Mechanistic three-dimensional model to study centrosome positioning in the interphase cell.

During the interphase in mammalian cells, the position of the centrosome is actively maintained at a small but finite distance away from the nucleus. The perinuclear positioning of the centrosome is crucial for cellular trafficking and progression into mitosis. Although the literature suggests that the contributions of the microtubule-associated forces bring the centrosome to the center of the cell, the position of the centrosome was merely investigated in the absence of the nucleus. Upon performing a coarse-grained simulation study with mathematical analysis, we show that the combined effect of the forces due to the cell cortex and the nucleus facilitate the centrosome positioning. Our study also demonstrates that in the absence of nucleus-based forces, the centrosome collapses on the nucleus due to cortical forces. Depending upon the magnitudes of the cortical forces and the nucleus-based forces, the centrosome appears to stay at various distances away from the nucleus. Such null force regions are found to be stable as well as unstable fixed points. This study uncovers a set of redundant schemes that the cell may adopt to produce the required cortical and nucleus-based forces stabilizing the centrosome at a finite distance away from the nucleus.

Spatio-temporal regulation of nuclear division by Aurora B kinase Ipl1 in Cryptococcus neoformans.

The nuclear division takes place in the daughter cell in the basidiomycetous budding yeast Cryptococcus neoformans. Unclustered kinetochores gradually cluster and the nucleus moves to the daughter bud as cells enter mitosis. Here, we show that the evolutionarily conserved Aurora B kinase Ipl1 localizes to the nucleus upon the breakdown of the nuclear envelope during mitosis in C. neoformans. Ipl1 is shown to be required for timely breakdown of the nuclear envelope as well. Ipl1 is essential for viability and regulates structural integrity of microtubules. The compromised stability of cytoplasmic microtubules upon Ipl1 depletion results in a significant delay in kinetochore clustering and nuclear migration. By generating an in silico model of mitosis, we previously proposed that cytoplasmic microtubules and cortical dyneins promote atypical nuclear division in C. neoformans. Improving the previous in silico model by introducing additional parameters, here we predict that an effective cortical bias generated by cytosolic Bim1 and dynein regulates dynamics of kinetochore clustering and nuclear migration. Indeed, in vivo alterations of Bim1 or dynein cellular levels delay nuclear migration. Results from in silico model and localization dynamics by live cell imaging suggests that Ipl1 spatio-temporally influences Bim1 or/and dynein activity along with microtubule stability to ensure timely onset of nuclear division. Together, we propose that the timely breakdown of the nuclear envelope by Ipl1 allows its own nuclear entry that helps in spatio-temporal regulation of nuclear division during semi-open mitosis in C. neoformans.