HIV-1 subtypes and latent reservoirs Ranga, Udaykumar; Panchapakesan, Arun; Saini, Chhavi
Current opinion in HIV & AIDS,
03/2024, Letnik:
19, Številka:
2
Journal Article
We explore the current status of research on HIV-1 subtype-specific variations and their impact on HIV-1 latency. We also briefly address the controversy surrounding the decision-making process ...governing the ON/OFF states of HIV-1 transcription, specifically focusing on the regulatory elements, the long terminal repeat (LTR), and Tat. Understanding the decision-making process is crucial for developing effective intervention strategies, such as the 'shock-and-kill' approach, to reactivate latent HIV-1.
Attention has been drawn to subtype-specific transcription factor binding site (TFBS) variations and the possible impact of these variations on viral latency. Further, diverse subtype-specific assays have been developed to quantify the latent viral reservoirs. One interesting observation is the relatively larger latent reservoirs in HIV-1B infection than those of other viral subtypes, which needs rigorous validation. The emergence of LTR-variant viral strains in HIV-1C demonstrating significantly higher levels of latency reversal has been reported.
Despite persistent and substantial efforts, latent HIV-1 remains a formidable challenge to a functional cure. Determined and continued commitment is needed to understand the ON/OFF decision-making process of HIV-1 latency, develop rigorous assays for accurately quantifying the latent reservoirs, and identify potent latency-reversing agents and cocktails targeting multiple latency stages. The review emphasizes the importance of including diverse viral subtypes in future latency research.
Efficient expression of functional proteins in heterologous hosts has become the pivotal focus of modern biotechnology and biomedical research. To this end, multiple alternatives to E. coli are being ...explored for recombinant protein expression. L. lactis, being a gram-positive organism, circumvents the need for an endotoxin removal step during protein purification. We report here the optimisation of the expression of HIV-1 Tat, a notoriously difficult protein, in Lactococcus lactis system. We evaluated five different promoters in two different Lactococcus lactis strains and examined the effect of pH, glucose, and induction time on the yield and purity of Tat. Finally, the recombinant Tat was functionally competent in transactivating the HIV-1 promoter in HLM-1 reporter cells. Our work provides a scaffold for future work on the expression of toxic proteins in Lactococcus lactis.
•Lactococcus lactis is a useful alternative to E. coli for expressing toxic proteins such as HIV-1 Tat.•P2 promoter is the best choice among PepN, NisA, P11, P170 and P2 promoters for HIV-1 Tat expression.•Expression through the P2 promoter demonstrates the least proteolytic processing in the MG1363 strain of L. lactis.•0.25% Glucose without pH adjustment serves as the best nutritional condition for HIV-1 Tat expression.
The study of host-pathogen interaction often requires interrogating the protein-protein interactions and examining post-translational modifications of the proteins. Traditional protein detection ...strategies are limited in their sensitivity, specificity, and multiplexing capabilities. The Proximity Ligation Assay (PLA), a versatile and powerful molecular technique, can overcome these limitations. PLA blends the specificity of antibodies, two antibodies detecting two different epitopes on the same or two different proteins, with the amplification efficiency of a polymerase to allow highly specific and sensitive detection of low-abundant proteins, protein-protein interactions, or protein modifications. In this protocol, we describe the application of PLA to detect the proximity between HIV-1 Tat with one of its cellular partners, p65, in an infected host cell. The protocol could be applied to any other context with slight modifications. Of note, PLA can only confirm the physical proximity between two epitopes or proteins; however, the proximity need not necessarily allude to the functional interaction between the two proteins.
Abstract
In order to enhance communication while minimizing complexity in 5G and beyond, MIMO‐OFDM systems need accurate channel prediction. In order to enhance channel prediction, decrease Error ...Vector Magnitude, Peak Power, and Adjacent Channel Leakage Ratio, this study employs the Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). Simplified clipping with filtering (SCF) reduces PAPR data, and this technique employs a PDACNN trained with the reduced data. By combining attention techniques with pyramidal dilated convolutions, the suggested PDACNN architecture is able to extract OFDM channel parameters across several scales. Attention approaches enhance channel prediction by allowing the model to dynamically concentrate on essential information. The primary objective is to make use of the network's ability to comprehend intricate spatial–temporal connections in OFDM channel data. The goal of these techniques is to make channel forecasts more accurate and resilient while decreasing concerns about EVM, Peak Power, and ACLR. To confirm the effectiveness of the suggested CP‐LSMIMO‐OFDM‐PDACNN, we measure its spectral efficiency, peak‐to‐average power ratio, bit error rate (BER), signal‐to‐noise ratio (SNR), and throughput. Throughput gains of 23.76%, 30.45%, and 18.97% are achieved via CP‐LSMIMO‐OFDM‐PDACNN, while bit error rates of 20.67%, 12.78%, and 19.56% are reduced. PAPRs of 21.66%, 23.09%, and 25.11% are also decreased.
Background
Inflammatory bowel disease (IBD) is increasingly diagnosed in South Asia. This survey by the Tamil Nadu Chapter of the Indian Society of Gastroenterology (TNISG) documents the demography, ...clinical profile, and therapeutic practices related to IBD in Tamil Nadu.
Methods
TNISG members from 32 institutions completed an online cross‐sectional questionnaire on IBD patients from March 2020 to January 2021.
Results
Of 1295 adult IBD patients, 654 had Crohn's disease (CD), 499 ulcerative colitis (UC), and 42 IBD‐unclassified (IBD‐U). CD and UC showed a unimodal age distribution. A total of 55% were graduates or postgraduates. A positive family history was noted in 30, other risk factors were uncommon. In CD, the pattern of involvement was ileocolonic (42.8%), ileal (34.7%), colonic (18.9%), and upper gastrointestinal (3.5%); while in UC, disease was characterized as extensive (44.9%), left‐sided (41.7%), or proctitis (13.4%). Perineal disease, perianal fistulae, and bowel obstruction were noted in 4.3, 14.0, and 23.5%, respectively, of CD. The most widely used drugs were mesalamine, azathioprine, and corticosteroids. Surgery was undertaken in 141 patients with CD and 23 patients with UC. Of the 138 patients with pediatric IBD (≤16 years), 23 were characterized as very early onset IBD (VEO‐IBD), 27 as early‐onset, and 88 as adolescent IBD. VEO‐IBD were more likely to have a positive family history of IBD and were more likely to have perineal disease and to have the IBD‐U phenotype. Among pediatric IBD patients, corticosteroids, mesalamine, and azathioprine were the most commonly used medications, while 25 pediatric patients received biologics.
Conclusion
This study provides important information on demography, clinical profile, and treatment practices of IBD in India.
Regional differences in inflammatory bowel disease (IBD) profile inform our understanding of the disease as well as prevention, and health planning. This study provides a snapshot of IBD presentation and practices in India at a time when the country is in the second of four phases of IBD transition. The apparently mild nature of IBD, the consequent widespread use of mesalamine, and the association of Crohn's disease with higher levels of education, compared to ulcerative colitis, are of notable interest.
Abstract
Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental ...and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website:
https://pages.nist.gov/jarvis_leaderboard/
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical ...approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard
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