TB and HIV induced immunosenescence: where do vaccines play a role?

This paper tackles the complex interplay between Human Immunodeficiency virus (HIV-1) and Mycobacterium tuberculosis (M. tuberculosis) infections, particularly their contribution to immunosenescence, the age-related decline in immune function. Using the current literature, we discuss the immunological mechanisms behind TB and HIV-induced immunosenescence and critically evaluate the BCG (Bacillus Calmette-Guérin) vaccine's role. Both HIV-1 and M. tuberculosis demonstrably accelerate immunosenescence: M. tuberculosis through DNA modification and heightened inflammation, and HIV-1 through chronic immune activation and T cell production compromise. HIV-1 and M. tuberculosis co-infection further hastens immunosenescence by affecting T cell differentiation, underscoring the need for prevention and treatment. Furthermore, the use of the BCG tuberculosis vaccine is contraindicated in patients who are HIV positive and there is a lack of investigation regarding the use of this vaccine in patients who develop HIV co-infection with possible immunosenescence. As HIV does not currently have a vaccine, we focus our review more so on the BCG vaccine response as a result of immunosenescence. We found that there are overall limitations with the BCG vaccine, one of which is that it cannot necessarily prevent re-occurrence of infection due to effects of immunosenescence or protect the elderly due to this reason. Overall, there is conflicting evidence to show the vaccine's usage due to factors involving its production and administration. Further research into developing a vaccine for HIV and improving the BCG vaccine is warranted to expand scientific understanding for public health and beyond.

How has the AI boom impacted algorithmic biology?

This Voices piece will highlight the impact of artificial intelligence on algorithm development among computational biologists. How has worldwide focus on AI changed the path of research in computational biology? What is the impact on the algorithmic biology research community?

Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity.

The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.

A benchmarked, high-efficiency prime editing platform for multiplexed dropout screening.

Prime editing installs precise edits into the genome with minimal unwanted byproducts, but low and variable editing efficiencies have complicated application of the approach to high-throughput functional genomics. Leveraging several recent advances, we assembled a prime editing platform capable of high-efficiency substitution editing across a set of engineered prime editing guide RNAs (epegRNAs) and corresponding target sequences (80% median intended editing). Then, using a custom library of 240,000 epegRNAs targeting >17,000 codons with 175 different substitution types, we benchmarked our platform for functional interrogation of small substitution variants (1-3 nucleotides) targeted to essential genes. Resulting data identified negative growth phenotypes for nonsense mutations targeted to ~8,000 codons, and comparing those phenotypes to results from controls demonstrated high specificity. We also observed phenotypes for synonymous mutations that disrupted splice site motifs at 3' exon boundaries. Altogether, we establish and benchmark a high-throughput prime editing approach for functional characterization of genetic variants with simple readouts from multiplexed experiments.

Protocol for the isolation of tumor cell-derived extracellular vesicles followed by in vivo metastasis assessment in a murine ovarian cancer model.

Extracellular vesicles (EVs) play a crucial role in facilitating communication between cancer cells and their immediate or remote microenvironments, thereby promoting the extensive spread of cancer throughout the body. In this context, we present a protocol for the isolation of tumor cell-derived EVs followed by in vivo metastasis assessment in a murine ovarian cancer model. We describe steps for the isolation and characterization of EVs from ID8 cells, development of a metastatic mouse model, and sample preparation for flow cytometry. For complete details on the use and execution of this protocol, please refer to Gupta et al.1.

Ensemble Detection of DNA Engineering Signatures.

Synthetic biology is creating genetically engineered organisms at an increasing rate for many potentially valuable applications, but this potential comes with the risk of misuse or accidental release. To begin to address this issue, we have developed a system called GUARDIAN that can automatically detect signatures of engineering in DNA sequencing data, and we have conducted a blinded test of this system using a curated Test and Evaluation (T&E) data set. GUARDIAN uses an ensemble approach based on the guiding principle that no single approach is likely to be able to detect engineering with perfect accuracy. Critically, ensembling enables GUARDIAN to detect sequence inserts in 13 target organisms with a high degree of specificity that requires no subject matter expert (SME) review.

From Gut to Hormones: Unraveling the Role of Gut Microbiota in (Phyto)Estrogen Modulation in Health and Disease.

The human gut microbiota regulates estrogen metabolism through the "estrobolome," the collection of bacterial genes that encode enzymes like ß-glucuronidases and ß-glucosidases. These enzymes deconjugate and reactivate estrogen, influencing circulating levels. The estrobolome mediates the enterohepatic circulation and bioavailability of estrogen. Alterations in gut microbiota composition and estrobolome function have been associated with estrogen-related diseases like breast cancer, enometrial cancer, and polycystic ovarian syndrome (PCOS). This is likely due to dysregulated estrogen signaling partly contributed by the microbial impacts on estrogen metabolism. Dietary phytoestrogens also undergo bacterial metabolism into active metabolites like equol, which binds estrogen receptors and exhibits higher estrogenic potency than its precursor daidzein. However, the ability to produce equol varies across populations, depending on the presence of specific gut microbes. Characterizing the estrobolome and equol-producing genes across populations can provide microbiome-based biomarkers. Further research is needed to investigate specific components of the estrobolome, phytoestrogen-microbiota interactions, and mechanisms linking dysbiosis to estrogen-related pathology. However, current evidence suggests that the gut microbiota is an integral regulator of estrogen status with clinical relevance to women's health and hormonal disorders.

A Reappraisal of the Antiviral Properties of and Immune Regulation through Dietary Phytochemicals.

In the present era of the COVID-19 pandemic, viral infections remain a major cause of morbidity and mortality worldwide. In this day and age, viral infections are rampant and spreading rapidly. Among the most aggressive viral infections are ebola, AIDS (acquired immunodeficiency syndrome), influenza, and SARS (severe acute respiratory syndrome). Even though there are few treatment options for viral diseases, most of the antiviral therapies are ineffective owing to frequent mutations, the development of more aggressive strains, drug resistance, and possible side effects. Traditionally, herbal remedies have been used by healers, including for dietary and medicinal purposes. Many clinical and scientific studies have demonstrated the therapeutic potential of plant-derived natural compounds. Because of unsafe practices like blood transfusions and organ transplants from infected patients, medical supply contamination. Our antiviral therapies cannot achieve sterile immunity, and we have yet to find a cure for these pernicious infections. Herbs have been shown to improve therapeutic efficacy against a wide variety of viral diseases because of their high concentration of immunomodulatory phytochemicals (both immunoinhibitory and anti-inflammatory). Combined with biotechnology, this folk medicine system can lead to the development of novel antiviral drugs and therapies. In this Review, we will summarize some selected bioactive compounds with probable mechanisms of their antiviral actions, focusing on the immunological axis of these compounds.

Network-augmented compartmental models to track asymptomatic disease spread.

Summary: A major challenge in understanding the spread of certain newly emerging viruses is the presence of asymptomatic cases. Their prevalence is hard to measure in the absence of testing tools, and yet the information is critical for tracking disease spread and shaping public health policies. Here, we introduce a framework that combines classic compartmental models with travel networks and we use it to estimate asymptomatic rates. Our platform, traSIR ("tracer"), is an augmented susceptible-infectious-recovered (SIR) model that incorporates multiple locations and the flow of people between them; it has a compartment model for each location and estimates of commuting traffic between compartments. TraSIR models both asymptomatic and symptomatic infections, as well as the dampening effect symptomatic infections have on traffic between locations. We derive analytical formulae to express the asymptomatic rate as a function of other key model parameters. Next, we use simulations to show that empirical data fitting yields excellent agreement with actual asymptomatic rates using only information about the number of symptomatic infections over time and compartments. Finally, we apply our model to COVID-19 data consisting of reported daily infections in the New York metropolitan area and estimate asymptomatic rates of COVID-19 to be ∼34%, which is within the 30-40% interval derived from widespread testing. Overall, our work demonstrates that traSIR is a powerful approach to express viral propagation dynamics over geographical networks and estimate key parameters relevant to virus transmission. Availability and implementation: No public repository.

Leveraging protein language models for accurate multiple sequence alignments.

Multiple sequence alignment (MSA) is a critical step in the study of protein sequence and function. Typically, MSA algorithms progressively align pairs of sequences and combine these alignments with the aid of a guide tree. These alignment algorithms use scoring systems based on substitution matrices to measure amino acid similarities. Although successful, standard methods struggle on sets of proteins with low sequence identity: the so-called twilight zone of protein alignment. For these difficult cases, another source of information is needed. Protein language models are a powerful new approach that leverages massive sequence data sets to produce high-dimensional contextual embeddings for each amino acid in a sequence. These embeddings have been shown to reflect physicochemical and higher-order structural and functional attributes of amino acids within proteins. Here, we present a novel approach to MSA, based on clustering and ordering amino acid contextual embeddings. Our method for aligning semantically consistent groups of proteins circumvents the need for many standard components of MSA algorithms, avoiding initial guide tree construction, intermediate pairwise alignments, gap penalties, and substitution matrices. The added information from contextual embeddings leads to higher accuracy alignments for structurally similar proteins with low amino-acid similarity. We anticipate that protein language models will become a fundamental component of the next generation of algorithms for generating MSAs.

Monkeypox virus: phylogenomics, host-pathogen interactome and mutational cascade.

While the world is still recovering from the Covid-19 pandemic, monkeypox virus (MPXV) awaits to cause another global outbreak as a challenge to all of mankind. However, the Covid-19 pandemic has taught us a lesson to speed up the pace of viral genomic research for the implementation of preventive and treatment strategies. One of the important aspects of MPXV that needs immediate insight is its evolutionary lineage based on genomic studies. Utilizing high-quality isolates from the GISAID (Global Initiative on Sharing All Influenza Data) database, primarily sourced from Europe and North America, we employed a SNP-based whole-genome phylogeny method and identified four major clusters among 628 MPXV isolates. Our findings indicate a distinct evolutionary lineage for the first MPXV isolate, and a complex epidemiology and evolution of MPXV strains across various countries. Further analysis of the host-pathogen interaction network revealed key viral proteins, such as E3, SPI-2, K7 and CrmB, that play a significant role in regulating the network and inhibiting the host's cellular innate immune system. Our structural analysis of proteins E3 and CrmB revealed potential disruption of stability due to certain mutations. While this study identified a large number of mutations within the new outbreak clade, it also reflected that we need to move fast with the genomic analysis of newly detected strains from around the world to develop better prevention and treatment methods.

Primate protein-ligand interfaces exhibit significant conservation and unveil human-specific evolutionary drivers.

Despite the vast phenotypic differences observed across primates, their protein products are largely similar to each other at the sequence level. We hypothesized that, since proteins accomplish all their functions via interactions with other molecules, alterations in the sites that participate in these interactions may be of critical importance. To uncover the extent to which these sites evolve across primates, we built a structurally-derived dataset of ~4,200 one-to-one orthologous sequence groups across 18 primate species, consisting of ~68,000 ligand-binding sites that interact with DNA, RNA, small molecules, ions, or peptides. Using this dataset, we identify functionally important patterns of conservation and variation within the amino acid residues that facilitate protein-ligand interactions across the primate phylogeny. We uncover that interaction sites are significantly more conserved than other sites, and that sites binding DNA and RNA further exhibit the lowest levels of variation. We also show that the subset of ligand-binding sites that do vary are enriched in components of gene regulatory pathways and uncover several instances of human-specific ligand-binding site changes within transcription factors. Altogether, our results suggest that ligand-binding sites have experienced selective pressure in primates and propose that variation in these sites may have an outsized effect on phenotypic variation in primates through pleiotropic effects on gene regulation.

Metabolic Reprogramming in Tumor-Associated Macrophages in the Ovarian Tumor Microenvironment.

The interaction between tumor cells and macrophages in the tumor microenvironment plays an essential role in metabolic changes in macrophages and reprograms them towards a pro-tumorigenic phenotype. Increasing evidence indicates that macrophage metabolism is a highly complex process and may not be as simple as previously thought. Pro-inflammatory stimuli switch macrophages towards an M1-like phenotype and rely mainly on aerobic glycolysis and fatty acid synthesis, whereas anti-inflammatory stimuli switch macrophages towards an M2-like phenotype. M2-like macrophages depend more on oxidative phosphorylation (OXPHOS) and fatty acid oxidation. However, this metabolically reprogrammed phenotypic switch in macrophages remained a mystery for a while. Therefore, through this review, we tend to describe how macrophage immunometabolism determines macrophage phenotypes and functions in tumor microenvironments (TMEs). Furthermore, we have discussed how metabolic reprogramming in TAM can be used for therapeutic intervention and drug resistance in ovarian cancer.

Learning probabilistic protein-DNA recognition codes from DNA-binding specificities using structural mappings.

Knowledge of how proteins interact with DNA is essential for understanding gene regulation. Although DNA-binding specificities for thousands of transcription factors (TFs) have been determined, the specific amino acid-base interactions comprising their structural interfaces are largely unknown. This lack of resolution hampers attempts to leverage these data in order to predict specificities for uncharacterized TFs or TFs mutated in disease. Here we introduce recognition code learning via automated mapping of protein-DNA structural interfaces (rCLAMPS), a probabilistic approach that uses DNA-binding specificities for TFs from the same structural family to simultaneously infer both which nucleotide positions are contacted by particular amino acids within the TF as well as a recognition code that relates each base-contacting amino acid to nucleotide preferences at the DNA positions it contacts. We apply rCLAMPS to homeodomains, the second largest family of TFs in metazoans and show that it learns a highly effective recognition code that can predict de novo DNA-binding specificities for TFs. Furthermore, we show that the inferred amino acid-nucleotide contacts reveal whether and how nucleotide preferences at individual binding site positions are altered by mutations within TFs. Our approach is an important step toward automatically uncovering the determinants of protein-DNA specificity from large compendia of DNA-binding specificities and inferring the altered functionalities of TFs mutated in disease.

The 1, 2-ethylenediamine SQ109 protects against tuberculosis by promoting M1 macrophage polarization through the p38 MAPK pathway.

Directly Observed Treatment Short-course (DOTs), is an effective and widely recommended treatment for tuberculosis (TB). The antibiotics used in DOTs, are immunotoxic and impair effector T cells, increasing the risk of re-infections and reactivation. Multiple reports suggest that addition of immune-modulators along with antibiotics improves the effectiveness of TB treatment. Therefore, drugs with both antimicrobial and immunomodulatory properties are desirable. N1-(Adamantan-2-yl)-N2-[(2E)-3,7-dimethylocta-2,6-dien-1-yl]ethane-1,2-diamine (SQ109) is an asymmetric diamine derivative of adamantane, that targets Mycobacterial membrane protein Large 3 (MmpL3). SQ109 dissipates the transmembrane electrochemical proton-gradient necessary for cell-wall biosynthesis and bacterial activity. Here, we examined the effects of SQ109 on host-immune responses using a murine TB model. Our results suggest the pro-inflammatory nature of SQ109, which instigates M1-macrophage polarization and induces protective pro-inflammatory cytokines through the p38-MAPK pathway. SQ109 also promotes Th1 and Th17-immune responses that inhibit the bacillary burden in a murine model of TB. These findings put forth SQ109 as a potential-adjunct to TB antibiotic therapy.

Neuronal identities derived by misexpression of the POU IV sensory determinant in a protovertebrate.

The protovertebrate Ciona intestinalis type A (sometimes called Ciona robusta) contains a series of sensory cell types distributed across the head-tail axis of swimming tadpoles. They arise from lateral regions of the neural plate that exhibit properties of vertebrate placodes and neural crest. The sensory determinant POU IV/Brn3 is known to work in concert with regional determinants, such as Foxg and Neurogenin, to produce palp sensory cells (PSCs) and bipolar tail neurons (BTNs), in head and tail regions, respectively. A combination of single-cell RNA-sequencing (scRNA-seq) assays, computational analysis, and experimental manipulations suggests that misexpression of POU IV results in variable transformations of epidermal cells into hybrid sensory cell types, including those exhibiting properties of both PSCs and BTNs. Hybrid properties are due to coexpression of Foxg and Neurogenin that is triggered by an unexpected POU IV feedback loop. Hybrid cells were also found to express a synthetic gene battery that is not coexpressed in any known cell type. We discuss these results with respect to the opportunities and challenges of reprogramming cell types through the targeted misexpression of cellular determinants.

Comparative genomic analysis reveals varying levels of mammalian adaptation to coronavirus infections.

Severe acute respiratory coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is of zoonotic origin. Evolutionary analyses assessing whether coronaviruses similar to SARS-CoV-2 infected ancestral species of modern-day animal hosts could be useful in identifying additional reservoirs of potentially dangerous coronaviruses. We reasoned that if a clade of species has been repeatedly exposed to a virus, then their proteins relevant for viral entry may exhibit adaptations that affect host susceptibility or response. We perform comparative analyses across the mammalian phylogeny of angiotensin-converting enzyme 2 (ACE2), the cellular receptor for SARS-CoV-2, in order to uncover evidence for selection acting at its binding interface with the SARS-CoV-2 spike protein. We uncover that in rodents there is evidence for adaptive amino acid substitutions at positions comprising the ACE2-spike interaction interface, whereas the variation within ACE2 proteins in primates and some other mammalian clades is not consistent with evolutionary adaptations. We also analyze aminopeptidase N (APN), the receptor for the human coronavirus 229E, a virus that causes the common cold, and find evidence for adaptation in primates. Altogether, our results suggest that the rodent and primate lineages may have had ancient exposures to viruses similar to SARS-CoV-2 and HCoV-229E, respectively.

Metabolite discovery through global annotation of untargeted metabolomics data.

Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak-peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.

Gene regulation of intracellular adhesion molecule-1 (ICAM-1): A molecule with multiple functions.

Intracellular adhesion molecule 1 (ICAM-1) is one of the most extensively studied inducible cell adhesion molecules which is responsible for several immune functions like T cell activation, extravasation, inflammation, etc. The molecule is constitutively expressed over the cell surface and is regulated up / down in response to inflammatory mediators like cellular stress, proinflammatory cytokines, viral infection. These stimuli modulate the expression of ICAM-1 primarily through regulating the ICAM-1 gene transcription. On account of the presence of various binding sites for NF-κB, AP-1, SP-1, and many other transcription factors, the architecture of the ICAM-1 promoter become complex. Transcription factors in union with other transcription factors, coactivators, and suppressors promote their assembly in a stereospecific manner on ICAM-1 promoter which mediates ICAM-1 regulation in response to different stimuli. Along with transcriptional regulation, epigenetic modifications also play a pivotal role in controlling ICAM-1 expression on different cell types. In this review, we summarize the regulation of ICAM-1 expression both at the transcriptional as well as post-transcriptional level with an emphasis on transcription factors and signaling pathways involved.