High Throughput Research Articles

Profiling of autoantibodies in the sera of glioblastoma patients.

Aim: This study aimed to determine the expression pattern of autoantibody proteins from the serum of grade IV glioblastoma patients.Materials & methods: We performed high throughput antibody profiling via the Sengenics i-Ome® Protein Array to determine the differentially expressed autoantibodies.Results: The results portrayed that anti-COL4A3BP and anti-HSP90AA1 were among the upregulated autoantibodies in glioblastoma sera.Conclusion: The selected autoantibodies offer promising targets for future glioblastoma pathogenesis. However, further validation is required to elucidate the autoantibody signature in glioblastoma patients.

Weak effects of prebiotically plausible peptides on self-triphosphorylation ribozyme function.

Catalytic RNAs (ribozymes) were central to early stages of life on earth. The first ribozymes probably emerged in the presence of prebiotically generated peptides because amino acids can be generated under abiotic conditions, and amino acids can oligomerize into peptides under prebiotically plausible conditions. Here we tested whether the presence of prebiotically plausible peptides could have aided the emergence of ribozymes, by an in vitro selection of self-triphosphorylation ribozymes from random sequence in the presence of ten different octapeptides. These peptides were composed of ten different, prebiotically plausible amino acids, each as mixture of d- and l-stereoisomers. After five rounds of selection and high throughput sequencing analysis, ten ribozymes that appeared most promising for peptide benefits were tested biochemically for possible benefits from each of the ten peptides. The strongest peptide benefit enhanced ribozyme activity by 2.6-fold, similar to the effect from an increase in the pH by one-half unit. Four arbitrarily chosen ribozymes from a previous selection without peptides showed no significant change in their activity in the presence of the ten peptides. Therefore, the used prebiotically plausible peptides - peptides without evolutionarily optimized sequence, without cationic or aromatic side chains - did not provide a strong benefit for the emergence of ribozyme activity. This finding stands in contrast to previously identified polycationic peptides, conjugates between peptides and polyaromatic hydrocarbons, and modern mRNA encoded proteins, all of which can strongly increase ribozyme function. The results are discussed in the context of origins of life.

AltAFplotter: a web app for reliable UPD detection in NGS diagnostics

BackgroundThe detection of uniparental disomies (the inheritance of both chromosome homologues from a single parent, UPDs) is not part of most standard or commercial NGS-pipelines in human genetics and thus a common gap in NGS diagnostics. To address this we developed a tool for UPD-detection based on panel or exome data which is easy to use and publicly available.ResultsThe app is freely available at https://altafplotter.uni-leipzig.de/ and implemented in Python, using the Streamlit framework for data science web apps. It utilizes bcftools and tabix for processing vcf files. The source code is available at https://github.com/HUGLeipzig/altafplotter and can be used to host your own instance of the tool.ConclusionWe believe the app to be a great benefit for research and diagnostic labs, which struggle identifying and interpreting UPDs in their NGS diagnostic setup. The information provided allows a quick interpretation of the results and thus is suitable for usage in a high throughput manner by clinicians and biologists.

Innovative Approach of High Throughput Screening in the Drug Discovery Quest for Chronic Bronchitis Treatment

Innovative Approach of High Throughput Screening in the Drug Discovery Quest for Chronic Bronchitis Treatment

High throughput detection of veterinary drug residues in chicken and eggs

Co-extraction of multiple types of target substances is the key to achieve high throughput detection. In this work, PDA@MOF-808/PAN NFsM was prepared by co-modified polyacrylonitrile nanofiber membrane (PAN NFsM) with polydopamine (PDA) and metal-organic framework-808 (MOF-808), and its potential as a solid-phase extraction (SPE) adsorbent was investigated by using the most typical nine veterinary drug residues in eggs and chicken as model target substances. The results show that PDA@MOF-808/PAN NFsM could effectively co-extract all the target substances (adsorption efficiency ranged from 81.46 % to 96.78 %), and had good capability of sample matrix purification (matrix effect was lower than −15.26 %), so a new SPE method has been established. Combined with UPLC-MS/MS, the detection limits were 0.3 to 3.1 μg/kg, the recoveries were between 71.02 % and 106.48 %, and the relative standard deviations were lower than 12.03 %, indicating that the method has considerable good sensitivity, accuracy and precision.

SIGHT—A System for Solvent‐Tight Incubation and Growth Monitoring in High Throughput

SIGHT—A System for Solvent‐Tight Incubation and Growth Monitoring in High Throughput

Connected Dominating Set Construction Using the Hunger Games Search Optimisation Algorithm in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a significant role in monitoring changes in the physical environment, relying on a subset of designated nodes known as the Connected Dominating Set (CDS) to facilitate communication and serve as the network's virtual backbone for routing and clustering. Despite the various algorithms for CDS construction, the challenges persist in terms of construction cost and scalability. In this context, this paper introduces the utilisation of the Hunger Games Search Optimisation Algorithm (HGSOA) for Connected Dominating Set Construction in Wireless Sensor Networks (HGSOA-CDS-WSN). The primary intention of this paper is “to develop a technique capable of constructing small-sized CDS with reduced construction costs, particularly suitable for uniformly or randomly distributed nodes”. The proposed HGSOA-CDS-WSN method is implemented in MATLAB based on various metrics. The results are compared with those of existing techniques, such as the Parallel Structure from Motion for UAV Pictures using Weighted CDS (PSfM-CDS-WSN), Polyhedra Associated with Locating-Dominating, Open Locating-Dominating, and Locating Total-Dominating Sets in Graphs (PAL-CDS-WSN), and Sustainable Maintenance of Connected Dominating Set by Solar Energy Harvesting for Internet of Things Networks (SHE-CDS-WSN). The proposed technique achieves lower delay by 13.24%, 25.64%, and 9.96%, and higher throughput by 18.34%, 24.55%, and 23.04% compared to the existing methods. This systematic evaluation underscores the efficacy and superiority of the proposed HGSOA-CDS-WSN technique in terms of connected dominating set size, construction time, energy efficiency, network stability, and overall performance, highlighting its potential to advance wireless sensor network applications.

Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments.

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have gained traction as a powerful model in cardiac disease and therapeutics research, since iPSCs are self-renewing and can be derived from healthy and diseased patients without invasive surgery. However, current iPSC-CM differentiation methods produce cardiomyocytes with immature, fetal-like electrophysiological phenotypes, and the variety of maturation protocols in the literature results in phenotypic differences between labs. Heterogeneity of iPSC donor genetic backgrounds contributes to additional phenotypic variability. Several mathematical models of iPSC-CM electrophysiology have been developed to help to predict cell responses, but these models individually do not capture the phenotypic variability observed in iPSC-CMs. Here, we tackle these limitations by developing a computational pipeline to calibrate cell preparation-specific iPSC-CM electrophysiological parameters. We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. To systematically optimize an experimental protocol that generates sufficient data for parameter calibration, we created in silico datasets by simulating various protocols applied to a population of models with known conductance variations, and then fitted parameters to those datasets. We found that calibrating to voltage and calcium transient data under 3 varied experimental conditions, including electrical pacing combined with ion channel blockade and changing buffer ion concentrations, improved model parameter estimates and model predictions of unseen channel block responses. This observation also held when the fitted data were normalized, suggesting that normalized fluorescence recordings, which are more accessible and higher throughput than patch clamp recordings, could sufficiently inform conductance parameters. Therefore, this computational pipeline can be applied to different iPSC-CM preparations to determine cell line-specific ion channel properties and understand the mechanisms behind variability in perturbation responses.

Hercules: Enabling Atomic Durability for Persistent Memory with Transient Persistence Domain

Persistent memory (pmem) products bring the persistence domain up to the memory level. Intel recently introduced the eADR feature that guarantees to flush data buffered in CPU cache to pmem on a power outage, thereby making the CPU cache a transient persistence domain . Researchers have explored how to enable the atomic durability for applications’ in-pmem data. In this article, we exploit the eADR-supported CPU cache to do so. A modified cache line, until written back to pmem, is a natural redo log copy of the in-pmem data. However, a write-back due to cache replacement or eADR on a crash overwrites the original copy. We accordingly developed Hercules, a hardware logging design for the transaction-level atomic durability, with supportive components installed in CPU cache, memory controller (MC), and pmem. When a transaction commits, Hercules commits on-chip its data staying in cache lines. For cache lines evicted before the commit, Hercules asks the MC to redirect and persist them into in-pmem log entries and commits them off-chip upon committing the transaction. Hercules lazily conducts pmem writes only for cache replacements at runtime. On a crash, Hercules saves metadata and data for active transactions into pmem for recovery. Experiments show that, by using CPU cache for both buffering and logging, Hercules yields much higher throughput and incurs significantly fewer pmem writes than state-of-the-art designs.

Genomic Sequence of a Czech Isolate of Erysimum Latent Virus from Sisymbrium altissimum.

The Erysimum latent virus (ELV), a tymovirus, was first isolated from several wild and cultivated brassicas in Germany. Its virions were shown to be serologically distinct from those of the turnip yellow mosaic virus (TYMV), which is also found in wild and cultivated plants in several European countries but also in other parts of the world. TYMV and ELV were among the first plant viruses to have had their genomes sequenced, and when other tymovirus genomes were sequenced, it was found that, in phylogenies, ELV is probably the basal outlier to all other tymoviruses. Here, we report the near-complete genomic sequence of another isolate of ELV from Czechia. This isolate was found in 1990 in Sisymbrium altissimum plants showing mosaic symptoms. It was detected using ELISA tests and electron microscopy. We have now sequenced the full coding sequence of this isolate using contemporary high throughput methods and found that the German and Czech isolates of ELV are closely related and are of the same virus species.

Microbial community profiling in intestinal tract of indigenous chickens from different villages

Village chickens (Gallus gallus domesticus) are commonly reared in rural households of South Africa and other developing countries. They play a vital role as a primary source of protein through the provision of meat and eggs. The chicken gut microbiota plays an important role in chicken’s immune system, its health, physiological development of the gut, digestion of food, nutrient absorption and productivity. Thus, it is imperative to critically investigate the chicken microbial composition in order to develop effective disease control measures and increase production. In this present study, microbial DNA was isolated from 34 non-descript mixed gender matured village chickens’ intestinal contents followed by high throughput Illumina sequencing targeting 16S rRNA gene. Senwamokgope village had the largest microbiota composition as compared to Itieleni and Thakgalang villages. Overall, Firmicutes (74%) was the most abundant phylum observed, followed by Proteobacteria (8%), Actinobateria (5%), and Bacteroidota (3%). At the genus level, Lactobacillus was the dominant bacteria. Other genera found included Sphingomonas (7%), Cutibacterium (4%), and Clostridium_sensu_stricto_1 (2%). The richness of female intestinal microbiota was higher compared to the male microbiota. The findings of this study provide baseline information that can assist to better understand the chicken gut microbiota and its interaction with diseases and parasites.

The prolyl isomerase FKBP11 is a secretory translocon accessory factor.

Eukaryotic cells encode thousands of secretory and membrane proteins, many of which are cotranslationally translocated into or across the endoplasmic reticulum (ER). Nascent polypeptides entering the ER encounter a network of molecular chaperones and enzymes that facilitate their folding. A rate-limiting step for some proteins is the trans-to-cis isomerization of the peptide bond between proline and the residue preceding it. The human ER contains six prolyl isomerases, but the function, organization and substrate range of these proteins is not clear. Here we show that the metazoan-specific, prolyl isomerase FKBP11 binds to ribosome-translocon complexes (RTCs) in the ER membrane, dependent on its single transmembrane domain (TMD) and a conserved, positively charged region at its cytosolic C-terminus. High throughput mRNA sequencing shows selective engagement with ribosomes synthesizing secretory and membrane proteins with long translocated segments, and functional analysis shows reduced stability of two such proteins, EpCAM and PTTG1IP, in cells depleted of FKBP11. We propose that FKBP11 is a translocon accessory factor that acts on a broad range of soluble secretory and transmembrane proteins during their synthesis at the ER.

Custom microfluidic chip design enables cost-effective three-dimensional spatiotemporal transcriptomics with a wide field of view.

Spatial transcriptomic techniques offer unprecedented insights into the molecular organization of complex tissues. However, integrating cost-effectiveness, high throughput, a wide field of view and compatibility with three-dimensional (3D) volumes has been challenging. Here we introduce microfluidics-assisted grid chips for spatial transcriptome sequencing (MAGIC-seq), a new method that combines carbodiimide chemistry, spatial combinatorial indexing and innovative microfluidics design. This technique allows sensitive and reproducible profiling of diverse tissue types, achieving an eightfold increase in throughput, minimal cost and reduced batch effects. MAGIC-seq breaks conventional microfluidics limits by enhancing barcoding efficiency and enables analysis of whole postnatal mouse sections, providing comprehensive cellular structure elucidation at near single-cell resolution, uncovering transcriptional variations and dynamic trajectories of mouse organogenesis. Our 3D transcriptomic atlas of the developing mouse brain, consisting of 93 sections, reveals the molecular and cellular landscape, serving as a valuable resource for neuroscience and developmental biology. Overall, MAGIC-seq is a high-throughput, cost-effective, large field of view and versatile method for spatial transcriptomic studies.

Exploring the sequence and structural determinants of the energy landscape from thermodynamically stable and kinetically trapped subtilisins: ISP1 and SbtE.

A protein's energy landscape, all the accessible conformations, their populations, and their dynamics of interconversion, is encoded in its primary sequence. While we have a good understanding of how a protein's primary sequence encodes its native state, we have a much weaker understanding of how sequence encodes the kinetic barriers such as unfolding and refolding. Here we have looked at two subtiliase homologs from the Bacillus subtilis, Intracellular Subtilisin Protease 1 (ISP1) and Subtilisin E (SbtE) that are expected to have very different dynamics. As an intracellular protein, ISP1 has a small pro-domain thought to act simply as a zymogen, whereas the extracellular SbtE has a large pro-domain required for folding. We examined the global and local energetics of the mature proteases and how each pro-domain impacts their landscapes. We find that ISP1's pro-domain has limited impact on the energy landscape while the mature SbtE is thermodynamically unstable and kinetically trapped. The impact of the pro-domain has opposite effects on the flexibility of the core of the protein. ISP1's core becomes more flexible while SbtE's core becomes more rigid. ISP1 contains a conserved amino-acid insertion not present in extracellular subtilisin proteases, which points to a potential source for these differences. These homologs are an extreme example of how changes in the primary sequence can dramatically alter a proteins energy landscape, both stability and dynamics, and highlight the need for large scale, high throughput studies on the relationship between primary sequence and conformational dynamics.

Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models.

Renal secretion plays an important role in excretion of drug from the kidney. Two major transporters known to be highly involved in renal secretion are MATE1/2 K and OCT2, the former of which is highly related to drug-drug interactions. Among published in silico models for MATE inhibitors, a previous model obtained a ROC-AUC value of 0.78 using high throughput percentage inhibition data [J. Med. Chem. 2013, 56(3), 781-795] which we aimed to improve upon here using a combined fingerprint and physics-based approach. To this end, we collected 225 publicly available compounds with pIC50 values against MATE1. Subsequently, on the one hand, we performed a physics-based approach using an Alpha-Fold protein structure, from which we obtained MM-GB/SA scores for those compounds. On the other hand, we built Random Forest (RF) and message passing neural network models using extended-connectivity fingerprints with radius 4 (ECFP4) and chemical structures as graphs, respectively, which also included MM-GB/SA scores as input variables. In a five-fold cross-validation with a separate test set, we found that the best predictivity for the hold-out test was observed in the RF model (including ECFP4 and MM-GB/SA data) with an ROC-AUC of 0.833 ± 0.036; while that of the MM-GB/SA regression model was 0.742. However, the MM-GB/SA model did not show a dependency of the performance on the particular chemical space being predicted. Additionally, via structural interaction fingerprint analysis, we identified interacting residues with inhibitor as identical for those with noninhibitors, including substrates, such as Gln49, Trp274, Tyr277, Tyr299, Ile303, and Tyr306. The similar binding modes are consistent with the observed similar IC50 value inhibitor when using different substrates experimentally, and practically, this can release the experimental scientists from bothering of selecting substrates for MATE1. Hence, we were able to build highly predictive classification models for MATE1 inhibitory activity with both ECFP4 and MM-GB/SA score as input features, which is fit-for-purpose for use in the drug discovery process.

Taxonomy of Pseudomonas spp. determines interactions with Bacillus subtilis.

There is a strong interest in the microbial ecology field to predict interaction among microorganisms, whether two microbial isolates will promote each other's growth or compete for resources. Numerous studies have been performed based on surveying the available literature or testing phylogenetically diverse sets of species in synthetic communities. Here, a high throughput screening has been performed using 720 Pseudomonas isolates, and their impact on the biofilm formation of Bacillus subtilis was tested. The aim was to determine whether a majority of Pseudomonas will promote or inhibit the biofilms of B. subtilis in the co-cultures. This study reports that Pseudomonas taxonomy is a good predictor of interaction outcome, and only a minority of Pseudomonas isolates promote Bacillus biofilm establishment.

Metagenomic analysis of soil from landfill site reveals a diverse microbial community involved in plastic degradation

In this study, we have investigated microbial communities structure and function using high throughput amplicon sequencing and whole metagenomic sequencing of DNA extracted from different depths of a plastic-laden landfill site. With diverse taxonomic groups inhabiting the plastic-rich soil, our study demonstrates the remarkable adaptability of microbes to use this new substrate as a carbon source. FTIR spectroscopic analysis of soil indicated degradation of plastic as perceived from the carbonyl index of 0.16, 0.72, and 0.44 at 0.6, 0.9 and 1.2 m depth, respectively. Similarly, water contact angles of 108.7 degree, 99.7 degree, 62.7 degree, and 77.8 degree of plastic pieces collected at 0.3, 0.6, 0.9, and 1.2 m depths respectively showed increased wettability and hydrophilicity of the plastic. Amplicon analysis of 16S and 18 S rRNA revealed a high abundance of several plastic-degrading bacterial groups, including Pseudomonas, Rhizobiales, Micrococcaceae, Chaetomium, Methylocaldum, Micromonosporaceae, Rhodothermaceae and fungi, including Trichoderma, Aspergillus, Candida at 0.9 m. The co-existence of specific microbial groups at different depths of landfill site indicates importance of bacterial and fungal interactions for plastic. Whole metagenome analysis of soil sample at 0.9 m depth revealed a high abundance of genes encoding enzymes that participate in the biodegradation of PVC, polyethylene, PET, and polyurethane. Curation of the pathways related to the degradation of these materials provided a blueprint for plastic biodegradation in this ecosystem. Altogether, our study has highlighted the importance of microbial cooperation for the biodegradation of pollutants. Our metagenome-based investigation supports the current perception that consortia of fungi-bacteria are preferable to axenic cultures for effective bioremediation of the environment.

Advanced DNA Amplification for Efficient Data Storage.

DNA amplification technologies have significantly advanced biotechnology, particularly in DNA storage. However, adaptation of these technologies to DNA storage poses substantial challenges. Key bottlenecks include achieving high throughput to manage large data sets, ensuring rapid and efficient DNA amplification, and minimizing bias to maintain data fidelity. This perspective begins with an overview of natural and artificial amplification strategies, such as polymerase chain reaction and isothermal amplification, highlighting their respective advantages and limitations. It then explores the prospective applications of these techniques in DNA storage, emphasizing the need to optimize protocols for scalability and robustness in handling diverse digital data. Concurrently, we identify promising avenues, including advancements in enzymatic processes and novel amplification methodologies, poised to mitigate existing constraints and propel the field forward. Ultimately, we provide insights into how to utilize advanced DNA amplification strategies poised to revolutionize the efficiency and feasibility of data storage, ushering in enhanced approaches to data retrieval in the digital age.

A novel user clustering and efficient resource allocation in non-orthogonal mutliple access for IoT networks.

Optimal resource allocation is crucial for 5G and beyond networks, especially when connecting numerous IoT devices. In this paper, user clustering and power allocation challenges in the downlink of a multi-carrier NOMA system are investigated, with sum rate as the optimization objective. The paper presents an iterative optimization process, starting with user clustering followed by power allocation of the users. Although the simultaneous transmission for multiple users achieves high system throughput in NOMA, it leads to more energy consumption, which is limited by the battery capacity of IoT devices. Enhancing energy efficiency by considering the QoS requirement is a primary challenge in NOMA-enabled IoT devices. Currently, fixed user clustering techniques are proposed without considering the diversity and heterogeneity of channels, leading to poor throughput performance. The proposed user clustering technique is based on the partial brute force search (P-BFS) method, which reduces complexity compared to the traditional exhaustive search method. After the user clustering, we performed optimal power allocation using the Lagrangian multiplier method with Karush-Kuhn-Tucker (KKT) optimal conditions for each user assigned to a subchannel in each cluster. Lastly, a deep neural network (DNN) based proposed P-BFS scheme is used to reduce resource allocation's complexity further. The simulation results show a significant improvement in the sum rate of the network.

Comparing newly developed SNP barcode panels with microsatellites to explore population genetics of malaria parasites in the Peruvian Amazon.

Malaria molecular surveillance (MMS) can provide insights into transmission dynamics, guiding national control/elimination programs. Considering the genetic differences among parasites from different areas in the Peruvian Amazon, we previously designed SNP barcode panels for Plasmodium vivax (Pv) and P. falciparum (Pf), integrated into AmpliSeq assays, to provide population genetics estimates of malaria parasites. These AmpliSeq assays are ideal for MMS: multiplexing different traits of interest, applicable to many use cases, and high throughput for large numbers of samples. The present study compares the genetic resolution of the SNP barcode panels in the AmpliSeq assays with widely used microsatellite (MS) panels to investigate Amazonian malaria parasites. Malaria samples collected in remote areas of the Peruvian Amazon (51 Pv & 80 Pf samples) were characterized using the Ampliseq assays and MS. Population genetics estimates (complexity of infection, genetic diversity and differentiation, and population structure) were compared using the SNP barcodes (Pv: 40 SNPs & Pf: 28 SNPs) and MS panels (Pv: 16 MS & Pf: 7 MS). The genetic diversity of Pv (expected heterozygosity, He ) was similar across the subpopulations for both makers: He MS = 0.68 - 0.78 (p = 0.23) and He SNP = 0.36 - 0.38 (p = 0.80). Pairwise genetic differentiation (fixation index, F ST ) was also comparable: F ST-MS = 0.04 - 0.14 and F ST-SNP = 0.03 - 0.12 (p = 0.34 - 0.85). No geographic clustering was observed with any panel. In addition, Pf genetic diversity trends ( He MS = 0 - 0.48 p = 0.03 - 1; He SNP = 0 - 0.09, p = 0.03 - 1) and pairwise F ST comparisons (F ST-MS = 0.14 - 0.65, F ST-SNP = 0.19 - 0.61, p = 0.24 - 0.83) were concordant between the panels. Similar population structure clustering was observed with both SNP and MS, highlighting one Pf subpopulation in an indigenous community. The SNP barcodes in the Pv AmpliSeq v2 Peru and Pf AmpliSeq v1 Peru assays offer comparable results to MS panels when investigating population genetics in Pv and Pv populations. Therefore, the AmpliSeq assays can efficiently characterize malaria transmission dynamics and population structure and support malaria elimination efforts in Peru.