Essential role for centromeric factors following p53 loss and oncogenic transformation.

In mammals, centromere definition involves the histone variant CENP-A (centromere protein A), deposited by its chaperone, HJURP (Holliday junction recognition protein). Alterations in this process impair chromosome segregation and genome stability, which are also compromised by p53 inactivation in cancer. Here we found that CENP-A and HJURP are transcriptionally up-regulated in p53-null human tumors. Using an established mouse embryonic fibroblast (MEF) model combining p53 inactivation with E1A or HRas-V12 oncogene expression, we reproduced a similar up-regulation of HJURP and CENP-A. We delineate functional CDE/CHR motifs within the Hjurp and Cenpa promoters and demonstrate their roles in p53-mediated repression. To assess the importance of HJURP up-regulation in transformed murine and human cells, we used a CRISPR/Cas9 approach. Remarkably, depletion of HJURP leads to distinct outcomes depending on their p53 status. Functional p53 elicits a cell cycle arrest response, whereas, in p53-null transformed cells, the absence of arrest enables the loss of HJURP to induce severe aneuploidy and, ultimately, apoptotic cell death. We thus tested the impact of HJURP depletion in pre-established allograft tumors in mice and revealed a major block of tumor progression in vivo. We discuss a model in which an "epigenetic addiction" to the HJURP chaperone represents an Achilles' heel in p53-deficient transformed cells.

Optimized nickase- and nuclease-based prime editing in human and mouse cells.

Precise genomic modification using prime editing (PE) holds enormous potential for research and clinical applications. In this study, we generated all-in-one prime editing (PEA1) constructs that carry all the components required for PE, along with a selection marker. We tested these constructs (with selection) in HEK293T, K562, HeLa and mouse embryonic stem (ES) cells. We discovered that PE efficiency in HEK293T cells was much higher than previously observed, reaching up to 95% (mean 67%). The efficiency in K562 and HeLa cells, however, remained low. To improve PE efficiency in K562 and HeLa, we generated a nuclease prime editor and tested this system in these cell lines as well as mouse ES cells. PE-nuclease greatly increased prime editing initiation, however, installation of the intended edits was often accompanied by extra insertions derived from the repair template. Finally, we show that zygotic injection of the nuclease prime editor can generate correct modifications in mouse fetuses with up to 100% efficiency.

Identification of Protein Isoforms Using Reference Databases Built from Long and Short Read RNA-Sequencing.

Alternative splicing can lead to distinct protein isoforms. These can have different functions in specific cells and tissues or in different developmental stages. In this study, we explored whether transcripts assembled from long read, nanopore-based, direct RNA-sequencing (RNA-seq) could improve the identification of protein isoforms in human K562 cells. By comparing with Illumina-based short read RNA-seq, we showed that a large proportion of Ensembl transcripts (5949/14,326) and genes expressing alternatively spliced transcripts (486/2981) identified with long direct reads were missed by short paired-end reads. By co-analyzing proteomic and transcriptomic data, we also showed that some peptides (826/35,976), proteins (262/3215), and protein isoforms arising from distinct transcript variants (574/1212) identified with isoform-specific peptides via custom long-read-based databases were missed in Illumina-derived databases. Finally, we generated unequivocal peptide evidence for a set of protein isoforms and showed that long read, direct RNA-seq allows the discovery of novel protein isoforms not already in reference databases or custom databases built from short read RNA-seq data. Our analysis highlights the benefits of long read RNA-seq data in the generation of reference databases to increase tandem mass spectrometry (MS/MS) identification of protein isoforms.

Evaluation of computational programs to predict HLA genotypes from genomic sequencing data.

Motivation: Despite being essential for numerous clinical and research applications, high-resolution human leukocyte antigen (HLA) typing remains challenging and laboratory tests are also time-consuming and labour intensive. With next-generation sequencing data becoming widely accessible, on-demand in silico HLA typing offers an economical and efficient alternative. Results: In this study we evaluate the HLA typing accuracy and efficiency of five computational HLA typing methods by comparing their predictions against a curated set of > 1000 published polymerase chain reaction-derived HLA genotypes on three different data sets (whole genome sequencing, whole exome sequencing and transcriptomic sequencing data). The highest accuracy at clinically relevant resolution (four digits) we observe is 81% on RNAseq data by PHLAT and 99% accuracy by OptiType when limited to Class I genes only. We also observed variability between the tools for resource consumption, with runtime ranging from an average of 5 h (HLAminer) to 7 min (seq2HLA) and memory from 12.8 GB (HLA-VBSeq) to 0.46 GB (HLAminer) per sample. While a minimal coverage is required, other factors also determine prediction accuracy and the results between tools do not correlate well. Therefore, by combining tools, there is the potential to develop a highly accurate ensemble method that is able to deliver fast, economical HLA typing from existing sequencing data.

VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9.

BACKGROUND: Natural variations in a genome can drastically alter the CRISPR-Cas9 off-target landscape by creating or removing sites. Despite the resulting potential side-effects from such unaccounted for sites, current off-target detection pipelines are not equipped to include variant information. To address this, we developed VARiant-aware detection and SCoring of Off-Targets (VARSCOT). RESULTS: VARSCOT identifies only 0.6% of off-targets to be common between 4 individual genomes and the reference, with an average of 82% of off-targets unique to an individual. VARSCOT is the most sensitive detection method for off-targets, finding 40 to 70% more experimentally verified off-targets compared to other popular software tools and its machine learning model allows for CRISPR-Cas9 concentration aware off-target activity scoring. CONCLUSIONS: VARSCOT allows researchers to take genomic variation into account when designing individual or population-wide targeting strategies. VARSCOT is available from https://github.com/BauerLab/VARSCOT .

Simultaneous two-color imaging in digital holographic microscopy.

We demonstrate the use of two-color digital holographic microscopy (DHM) for imaging microbiological subjects. The use of two wavelengths significantly reduces artifacts present in the reconstructed data, allowing us to image weakly-scattering objects in close proximity to strongly-scattering objects. We demonstrate this by reconstructing the shape of the flagellum of a unicellular eukaryotic parasite Leishmania mexicana in close proximity to a more strongly-scattering cell body. Our approach also yields a reduction of approximately one third in the axial position uncertainty when tracking the motion of swimming cells at low magnification, which we demonstrate with a sample of Escherichia coli bacteria mixed with polystyrene beads. The two-wavelength system that we describe introduces minimal additional complexity into the optical system, and provides significant benefits.

Flagellated bacterial motility in polymer solutions.

It is widely believed that the swimming speed, v, of many flagellated bacteria is a nonmonotonic function of the concentration, c, of high-molecular-weight linear polymers in aqueous solution, showing peaked v(c) curves. Pores in the polymer solution were suggested as the explanation. Quantifying this picture led to a theory that predicted peaked v(c) curves. Using high-throughput methods for characterizing motility, we measured v and the angular frequency of cell body rotation, Ω, of motile Escherichia coli as a function of polymer concentration in polyvinylpyrrolidone (PVP) and Ficoll solutions of different molecular weights. We find that nonmonotonic v(c) curves are typically due to low-molecular-weight impurities. After purification by dialysis, the measured v(c) and Ω(c) relations for all but the highest-molecular-weight PVP can be described in detail by Newtonian hydrodynamics. There is clear evidence for non-Newtonian effects in the highest-molecular-weight PVP solution. Calculations suggest that this is due to the fast-rotating flagella seeing a lower viscosity than the cell body, so that flagella can be seen as nano-rheometers for probing the non-Newtonian behavior of high polymer solutions on a molecular scale.

The Aerotactic Response of Caulobacter crescentus.

Many motile microorganisms are able to detect chemical gradients in their surroundings to bias their motion toward more favorable conditions. In this study, we observe the swimming patterns of Caulobacter crescentus, a uniflagellated bacterium, in a linear oxygen gradient produced by a three-channel microfluidic device. Using low-magnification dark-field microscopy, individual cells are tracked over a large field of view and their positions within the oxygen gradient are recorded over time. Motor switching events are identified so that swimming trajectories are deconstructed into a series of forward and backward swimming runs. Using these data, we show that C. crescentus displays aerotactic behavior by extending the average duration of forward swimming runs while moving up an oxygen gradient, resulting in directed motility toward oxygen sources. Additionally, the motor switching response is sensitive both to the steepness of the gradient experienced and to background oxygen levels, exhibiting a logarithmic response.

Ecological influences on the behaviour and fertility of malaria parasites.

BACKGROUND: Sexual reproduction in the mosquito is essential for the transmission of malaria parasites and a major target for transmission-blocking interventions. Male gametes need to locate and fertilize females in the challenging environment of the mosquito blood meal, but remarkably little is known about the ecology and behaviour of male gametes. METHODS: Here, a series of experiments explores how some aspects of the chemical and physical environment experienced during mating impacts upon the production, motility, and fertility of male gametes. RESULTS AND CONCLUSIONS: Specifically, the data confirm that: (a) rates of male gametogenesis vary when induced by the family of compounds (tryptophan metabolites) thought to trigger gamete differentiation in nature; and (b) complex relationships between gametogenesis and mating success exist across parasite species. In addition, the data reveal that (c) microparticles of the same size as red blood cells negatively affect mating success; and (d) instead of swimming in random directions, male gametes may be attracted by female gametes. Understanding the mating ecology of malaria parasites, may offer novel approaches for blocking transmission and explain adaptation to different species of mosquito vectors.

High-speed holographic microscopy of malaria parasites reveals ambidextrous flagellar waveforms.

Axonemes form the core of eukaryotic flagella and cilia, performing tasks ranging from transporting fluid in developing embryos to the propulsion of sperm. Despite their abundance across the eukaryotic domain, the mechanisms that regulate the beating action of axonemes remain unknown. The flagellar waveforms are 3D in general, but current understanding of how axoneme components interact stems from 2D data; comprehensive measurements of flagellar shape are beyond conventional microscopy. Moreover, current flagellar model systems (e.g., sea urchin, human sperm) contain accessory structures that impose mechanical constraints on movement, obscuring the "native" axoneme behavior. We address both problems by developing a high-speed holographic imaging scheme and applying it to the (male) microgametes of malaria (Plasmodium) parasites. These isolated flagella are a unique, mathematically tractable model system for the physics of microswimmers. We reveal the 3D flagellar waveforms of these microorganisms and map the differential shear between microtubules in their axonemes. Furthermore, we overturn claims that chirality in the structure of the axoneme governs the beat pattern [Hirokawa N, et al. (2009) Ann Rev Fluid Mech 41:53-72], because microgametes display a left- or right-handed character on alternate beats. This breaks the link between structural chirality in the axoneme and larger scale symmetry breaking (e.g., in developing embryos), leading us to conclude that accessory structures play a critical role in shaping the flagellar beat.

A novel splicing outcome reveals more than 2000 new mammalian protein isoforms.

MOTIVATION: We have recently characterized an instance of alternative splicing that differs from the canonical gene transcript by deletion of a length of sequence not divisible by three, but where translation can be rescued by an alternative start codon. This results in a predicted protein in which the amino terminus differs markedly in sequence from the known protein product(s), as it is translated from an alternative reading frame. Automated pipelines have annotated thousands of splice variants but have overlooked these protein isoforms, leading to them being underrepresented in current databases. RESULTS: Here we describe 1849 human and 733 mouse transcripts that can be transcribed from an alternate ATG. Of these, >80% have not been annotated previously. Those conserved between human and mouse genomes (and hence under likely evolutionary selection) are identified. We provide mass spectroscopy evidence for translation of selected transcripts. Of the described splice variants, only one has previously been studied in detail and converted the encoded protein from an activator of cell-function to a suppressor, demonstrating that these splice variants can result in profound functional change. We investigate the potential functional effects of this splicing using a variety of bioinformatic tools. The 2582 variants we describe are involved in a wide variety of biological processes, and therefore open many new avenues of research.

Prevalence of lower extremity peripheral artery disease among adult diabetes patients in southwestern Uganda.

BACKGROUND: Peripheral artery disease (PAD) is a major complication of atherosclerosis. PAD can be diagnosed with low-cost diagnostic techniques and is associated with significant morbidity and mortality. While the major epidemiologic risk factors for PAD have been established in the western world, data from resource-poor countries are limited. We performed a cross-sectional study to determine the prevalence and correlates of PAD among patients with diabetes at Mbarara Referral Hospital in southwestern Uganda. METHODS: We consecutively enrolled diabetes patients aged 50 years or greater presenting to the outpatient clinic. We collected blood for fasting lipid profile, HIV serology, and glycosylated hemoglobin, measured blood pressure and ankle brachial index, and administered the Edinburgh Claudication Questionnaire (ECQ). We also surveyed patients for other PAD risk factors. We used logistic regression to determine correlates of PAD. RESULTS: We enrolled 229 diabetes patients. The median age of 60 years (IQR 55-66), and 146 (63.7%) were female. Fifty five patients (24%) had PAD (ABI of ≤ 0.9). Of these, 48 /55 (87.27%) had mild PAD (ABI 0.71-0.9) while 7/55 (12.73%) had moderate to severe PAD (ABI < 0.7). Amongst those with PAD, 24/55 (43.64%) reported claudication by the ECQ. Correlates of PAD included female sex (AOR 2.25, 95% CI 1.06 - 4.77, p = 0.034), current high blood pressure (AOR 2.59, 95% CI 1.25-5.33, p = 0.01), and being on a sulfonylurea-glibenclamide (AOR 3.47, 95% CI 1.55 - 7.76, p = 0.002). CONCLUSION: PAD was common in diabetic patients in southwestern Uganda. Given its low cost and ease of measurement, ABI deserves further assessment as a screening tool for both PAD and long term cardiovascular risk amongst diabetics in this region.

Data Visualization of CRISPR-Cas9 Guide RNA Design Tools.

With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and minimum off-target activity (specificity) is now an essential part of genome editing experiments. Today, a variety of tools exist that allow the search of genomic targets and let users customize their search parameters to better suit their experiments. Here we present an overview of different ways to visualize these searched CRISPR target sites along with specific downstream information like primer design, restriction enzyme activity and mutational outcome prediction after a double-stranded break. We discuss the importance of a good visualization summary to interpret information along with different ways to represent similar information effectively.

Mannose-Presenting "Glyco-Colicins" Convert the Bacterial Cell Surface into a Multivalent Adsorption Site for Adherent Bacteria.

Biofilm formation is integral to the pathogenesis of numerous adherent bacteria and contributes to antimicrobial resistance (AMR). The rising threat of AMR means the need to develop novel nonbactericidal antiadhesion approaches against such bacteria is more urgent than ever. Both adherent-invasive Escherichia coli (AIEC, implicated in inflammatory bowel disease) and uropathogenic E. coli (UPEC, responsible for ∼80% of urinary tract infections) adhere to terminal mannose sugars on epithelial glycoproteins through the FimH adhesin on their type 1 pilus. Although mannose-based inhibitors have previously been explored to inhibit binding of adherent bacteria to epithelial cells, this approach has been limited by monovalent carbohydrate-protein interactions. Herein, we pioneer a novel approach to this problem through the preparation of colicin E9 bioconjugates that bind to the abundant BtuB receptor in the outer membrane of bacteria, which enables multivalent presentation of functional motifs on the cell surface. We show these bioconjugates label the surface of live E. coli and furthermore demonstrate that mannose-presenting "glyco-colicins" induce E. coli aggregation, thereby using the bacteria, itself, as a multivalent platform for mannose display, which triggers binding to adjacent FimH-presenting bacteria.

Real-time 3D tracking of swimming microbes using digital holographic microscopy and deep learning.

The three-dimensional swimming tracks of motile microorganisms can be used to identify their species, which holds promise for the rapid identification of bacterial pathogens. The tracks also provide detailed information on the cells' responses to external stimuli such as chemical gradients and physical objects. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data. We demonstrate that a common neural network (NN) accelerates the analysis of holographic data by an order of magnitude, enabling its use on single-board computers and in real time. We establish a heuristic relationship between the distance of a cell from the focal plane and the size of the bounding box assigned to it by the NN, allowing us to rapidly localise cells in three dimensions as they swim. This technique opens the possibility of providing real-time feedback in experiments, for example by monitoring and adapting the supply of nutrients to a microbial bioreactor in response to changes in the swimming phenotype of microbes, or for rapid identification of bacterial pathogens in drinking water or clinical samples.

Telehealth trends and the challenge for infrastructure.

BACKGROUND: As telehealth takes advantage of improved networks, there is a growing need to understand the infrastructure needs of future telehealth developments. This work aims to predict such needs based on current trends and research. MATERIALS AND METHODS: We conducted a literature review of telehealth with a focus on advanced network infrastructure. We drew inferences from our previous demonstrator projects in advanced telehealth, but the most important findings emerged from interviews with a panel of thought leaders. RESULTS: Our results show that there will be simultaneous and coupled evolution of telehealth through the space spanned by three axes: care models, clinical applications, and technology. We also consider a two-dimensional model of reach and complexity to describe future applications. Universal access to advanced networks will drive fundamental changes in healthcare deliver. The biggest change will be seen in home and mobile health care delivery, forming part of a trend toward patient-centric models. Other aspects of decentralization in healthcare systems will include networks of caregivers. Besides this reach trend, the complexity trend will include integrating multiple-channel applications and seamlessly moving large datasets in real time among hospitals, other medical facilities, and homes. There is a need to provide infrastructure that does not have an upper limit on quality of service and allows telehealth to address mobility, usability, interoperability, intelligence, and adaptability in a systematic way.

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19.

Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, productivity, and the world economy. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and further visualises and compares the results using selected dimensionality reduction methods that include principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for selected variants (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We find that the proposed framework can effectively distinguish between the major variants and has the potential to identify emerging variants in the future.

Recapitulation of Skewed X-Inactivation in Female Ornithine Transcarbamylase-Deficient Primary Human Hepatocytes in the FRG Mouse: A Novel System for Developing Epigenetic Therapies.

Realization of the immense therapeutic potential of epigenetic editing requires development of clinically predictive model systems that faithfully recapitulate relevant aspects of the target disease pathophysiology. In female patients with ornithine transcarbamylase (OTC) deficiency, an X-linked condition, skewed inactivation of the X chromosome carrying the wild-type OTC allele is associated with increased disease severity. The majority of affected female patients can be managed medically, but a proportion require liver transplantation. With rapid development of epigenetic editing technology, reactivation of silenced wild-type OTC alleles is becoming an increasingly plausible therapeutic approach. Toward this end, privileged access to explanted diseased livers from two affected female infants provided the opportunity to explore whether engraftment and expansion of dissociated patient-derived hepatocytes in the FRG mouse might produce a relevant model for evaluation of epigenetic interventions. Hepatocytes from both infants were successfully used to generate chimeric mouse-human livers, in which clusters of primary human hepatocytes were either OTC positive or negative by immunohistochemistry (IHC), consistent with clonal expansion from individual hepatocytes in which the mutant or wild-type OTC allele was inactivated, respectively. Enumeration of the proportion of OTC-positive or -negative human hepatocyte clusters was consistent with dramatic skewing in one infant and minimal to modest skewing in the other. Importantly, IHC and fluorescence-activated cell sorting analysis of intact and dissociated liver samples from both infants showed qualitatively similar patterns, confirming that the chimeric mouse-human liver model recapitulated the native state in each infant. Also of importance was the induction of a treatable metabolic phenotype, orotic aciduria, in mice, which correlated with the presence of clonally expanded OTC-negative primary human hepatocytes. We are currently using this unique model to explore CRISPR-dCas9-based epigenetic targeting strategies in combination with efficient adeno-associated virus (AAV) gene delivery to reactivate the silenced functional OTC gene on the inactive X chromosome.

Assessment of Pre-Clinical Liver Models Based on Their Ability to Predict the Liver-Tropism of Adeno-Associated Virus Vectors.

The liver is a prime target for in vivo gene therapies using recombinant adeno-associated viral vectors. Multiple clinical trials have been undertaken for this target in the past 15 years; however, we are still to see market approval of the first liver-targeted adeno-associated virus (AAV)-based gene therapy. Inefficient expression of the therapeutic transgene, vector-induced liver toxicity and capsid, and/or transgene-mediated immune responses reported at high vector doses are the main challenges to date. One of the contributing factors to the insufficient clinical outcomes, despite highly encouraging preclinical data, is the lack of robust, biologically and clinically predictive preclinical models. To this end, this study reports findings of a functional evaluation of 6 AAV vectors in 12 preclinical models of the human liver, with the aim to uncover which combination of models is the most relevant for the identification of AAV capsid variant for safe and efficient transgene delivery to primary human hepatocytes. The results, generated by studies in models ranging from immortalized cells, iPSC-derived and primary hepatocytes, and primary human hepatic organoids to in vivo models, increased our understanding of the strengths and weaknesses of each system. This should allow the development of novel gene therapies targeting the human liver.