PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning

PLEKHG1: New Potential Candidate Gene for Periventricular White Matter Abnormalities

Hypoxic-ischemic brain damage presents a significant neurological challenge, often manifesting during the perinatal period. Specifically, periventricular leukomalacia (PVL) is emerging as a notable contributor to cerebral palsy and intellectual disabilities. It compromises cerebral microcirculation, resulting in insufficient oxygen or blood flow to the periventricular region of the brain. As widely documented, these pathological conditions can be caused by several factors encompassing preterm birth (4-5% of the total cases), as well single cotwin abortion and genetic variants such as those associated with GTPase pathways. Whole exome sequencing (WES) analysis identified a de novo causative variant within the pleckstrin homology domain-containing family G member 1 (PLEKHG1) gene in a patient presenting with PVL. The PLEKHG1 gene is ubiquitously expressed, showing high expression patterns in brain tissues. PLEKHG1 is part of a family of Rho guanine nucleotide exchange factors, and the protein is essential for cell division control protein 42 (CDC42) activation in the GTPase pathway. CDC42 is a key small GTPase of the Rho-subfamily, regulating various cellular functions such as cell morphology, migration, endocytosis, and cell cycle progression. The molecular mechanism involving PLEKHG1 and CDC42 has an intriguing role in the reorientation of cells in the vascular endothelium, thus suggesting that disruption responses to mechanical stress in endothelial cells may be involved in the formation of white matter lesions. Significantly, CDC42 association with white matter abnormalities is underscored by its MIM phenotype number. In contrast, although PLEKHG1 has been recently associated with patients showing white matter hyperintensities, it currently lacks a MIM phenotype number. Additionally, in silico analyses classified the identified variant as pathogenic. Although the patient was born prematurely and subsequently to dichorionic gestation, during which its cotwin died, we suggest that the variant described can strongly contribute to PVL. The aim of the current study is to establish a plausible association between the PLEKHG1 gene and PVL.

Structural and mechanistic diversity in p53-mediated regulation of organismal longevity across taxonomical orders

The accumulation of senescent cells induces several aging phenotypes, and the p53 tumor suppressor protein regulates one of the two known cellular senescence pathways. p53's regulation of senescence is however not clear. For example, p53 deficiency in some mice has been shown to rescue premature aging while others display significant aging phenotype when p53-deficient. This study seeks to elucidate, structurally and mechanistically, p53's roles in longevity. Through a relative evolutionary scoring (RES) algorithm, we quantify the level of evolutionary change in the residues of p53 across organisms of varying average lifespans in six taxonomic orders. Secondly, we used PEPPI to assess the likelihood of interaction between p53-or p53-linked proteins-and known senescence-regulating proteins across organisms in the orders Primates and Perciformes. Our RES algorithm found variations in the alignments within and across orders, suggesting that mechanisms of p53-mediated regulation of longevity may vary. PEPPI results suggest that longer-lived species may have evolved to regulate induction and inhibition of cellular senescence better than their shorter-lived counterparts. With experimental verification, these predictions could help elucidate the mechanisms of p53-mediated cellular senescence, ultimately clarifying our understanding of p53's connection to aging in a multiple-species context.

Representing core gene expression activity relationships using the latent structure implicit in Bayesian networks

MOTIVATION

Many types of networks, such as co-expression or ChIP-seq-based gene-regulatory networks, provide useful information for biomedical studies. However, they are often too full of connections and difficult to interpret, forming "indecipherable hairballs."

RESULTS

To address this issue, we propose that a Bayesian network can summarize the core relationships between gene expression activities. This network, which we call the LatentDAG, is substantially simpler than conventional co-expression network and ChIP-seq networks (by two orders of magnitude). It provides clearer clusters, without extraneous cross-cluster connections, and clear separators between modules. Moreover, one can find a number of clear examples showing how it bridges the connection between steps in the transcriptional regulatory network and other networks (e.g. RNA-binding protein). In conjunction with a graph neural network, the LatentDAG works better than other biological networks in a variety of tasks, including prediction of gene conservation and clustering genes.

AVAILABILITY AND IMPLEMENTATION

Code is available at https://github.com/gersteinlab/LatentDAG.

Light reception of Phycomyces revisited: several white collar proteins confer blue- and red-light sensitivity and control dynamic range and adaptation

The giant-fruiting body, sporangiophore, of the fungus Phycomyces blakesleeanus grows toward near-UV/blue-light (phototropism). The blue-light photoreceptor, MadA, should contain FAD bound to the LOV domain, and forms a complex with MadB. Both proteins are homologs of white collar proteins WC-1 and WC-2 from the fungus Neurospora crassa and should be localized in nuclei, where they function as a light-sensitive transcription factor complex. The photoreceptor properties of two further Wc proteins, WcoA and WcoB, remain unclear because of lack of mutants. We propose that WcoA and/or WcoB play essential roles in photoreception by enlarging the dynamic range that help explain complex stimulus-response relationships. Even though red light does not elicit photo-movement or -differentiation in Phycomyces, it affects the effectiveness of blue light which indicates an underlying photochromic receptor. Protein sequence searches show that other fungal red-light receptors are absent in Phycomyces. The solution to the red-light riddle is thus sought in the ability of Wc complexes to generate after blue-light irradiation a neutral flavosemiquinone radical that absorbs red light and functions as primary photochemical signal. Phototropism requires Ras-GAP (MadC) as part of the signal transduction cascade and, we propose, to allocate photoreceptors in the plasmalemma of the growing zone, which allows for receptor dichroism, range adjustment and contrast recognition for spatial orientation. Phototropic signal chains must entail transduction networks between Wc receptors and small G-proteins and their associated Ras-GAP and Ras-GEF proteins. The interactions among these proteins should occur in trans-Golgi vesicles and the plasmalemma of the growing zone.

Transcription Factor 23 is an Essential Determinant of Murine Term Parturition

Pregnancy involving intricate tissue transformations governed by the progesterone hormone (P4). P4 signaling via P4 receptors (PRs) is vital for endometrial receptivity, decidualization, myometrial quiescence, and labor initiation. This study explored the role of TCF23 as a downstream target of PR during pregnancy. TCF23 was found to be expressed in female reproductive organs, predominantly in uterine stromal and smooth muscle cells. Tcf23 expression was high during midgestation and was specifically regulated by P4, but not estrogen. The Tcf23 knockout (KO) mouse was generated and analyzed. Female KO mice aged 4-6 months exhibited subfertility, reduced litter size, and defective parturition. Uterine histology revealed disrupted myometrial structure, altered collagen organization, and disarrayed smooth muscle sheets at the conceptus sites of KO mice. RNA-Seq analysis of KO myometrium revealed dysregulation of genes associated with cell adhesion and extracellular matrix organization. TCF23 potentially modulates TCF12 activity to mediate cell-cell adhesion and matrix modulation in smooth muscle cells. Overall, TCF23 deficiency leads to impaired myometrial remodeling, causing parturition delay and fetal demise. This study sheds light on the critical role of TCF23 as a dowstream mediator of PR in uterine remodeling, reflecting the importance of cell-cell communication and matrix dynamics in myometrial activation and parturition.

The power of computational proteomics platforms to decipher protein-protein interactions

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.

Gene-edited protein kinases and phosphatases in molecular plant breeding

Protein phosphorylation, the most common and essential post-translational modification, belongs to crucial regulatory mechanisms in plants, affecting their metabolism, intracellular transport, cytoarchitecture, cell division, growth, development, and interactions with the environment. Protein kinases and phosphatases, two important families of enzymes optimally regulating phosphorylation, have now become important targets for gene editing in crops. We review progress on gene-edited protein kinases and phosphatases in crops using clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9). We also provide guidance for computational prediction of alterations and/or changes in function, activity, and binding of protein kinases and phosphatases as consequences of CRISPR/Cas9-based gene editing with its possible application in modern crop molecular breeding towards sustainable agriculture.

In Silico Analysis of Protein-Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe

Ermp1 is a putative metalloprotease from Schizosaccharomyces pombe and a member of the Fxna peptidases. Although their function is unknown, orthologous proteins from rats and humans have been associated with the maturation of ovarian follicles and increased ER stress. This study focuses on proposing the first prediction of PPI by comparison of the interologues between humans and yeasts, as well as the molecular docking and dynamics of the M28 domain of Ermp1 with possible target proteins. As results, 45 proteins are proposed that could interact with the metalloprotease. Most of these proteins are related to the transport of Ca2+ and the metabolism of amino acids and proteins. Docking and molecular dynamics suggest that the M28 domain of Ermp1 could hydrolyze leucine and methionine residues of Amk2, Ypt5 and Pex12. These results could support future experimental investigations of other Fxna peptidases, such as human ERMP1.

Revolutionizing protein-protein interaction prediction with deep learning

Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning methods, coupled with the vast genomic sequence data, have significantly boosted the accuracy of predicting protein structures and modeling protein complexes, approaching levels comparable to experimental techniques. Herein, we review the latest advances in the computational methods for modeling 3D protein complexes and the prediction of protein interaction partners, emphasizing the application of deep learning methods deriving from coevolution analysis. The review also highlights biomedical applications of PPI prediction and outlines challenges in the field.

RNA interaction format: a general data format for RNA interactions

SUMMARY

RNA molecules play crucial roles in various biological processes. They mediate their function mainly by interacting with other RNAs or proteins. At present, information about these interactions is distributed over different resources, often providing the data in simple tab-delimited formats that differ between the databases. There is no standardized data format that can capture the nature of all these different interactions in detail.

AVAILABILITY AND IMPLEMENTATION

Here, we propose the RNA interaction format (RIF) for the detailed representation of RNA-RNA and RNA-Protein interactions and provide reference implementations in C/C++, Python, and JavaScript. RIF is released under licence GNU General Public License version 3 (GNU GPLv3) and is available on https://github.com/RNABioInfo/rna-interaction-format.

A novel functional peptide, named EQ-9 (ESETRILLQ), identified by virtual screening from regenerative cell secretome and its potential anti-aging and restoration effects in topical applications

Skin aging refers to a degenerative process that can be affected and regulated by intrinsic and extrinsic factors. The mesenchymal stem cell secretome covers a considerable number of regenerative molecules with anti-aging effects in a wide variety of circumstances. However, it is complex, time-consuming, and costly to identify specific compounds from thousands of natural molecules using conventional methods. With the development of computational biology and machine learning, an efficient workflow was generated to identify novel peptides with anti-aging and skin restoration potential. One of the candidate peptides was discovered and subsequently truncated to a novel peptide named EQ-9, with promising anti-aging effects for topical applications at a concentration of 10 ppm validated by experimental validation. The above-described paradigm is expected to be further applied to the virtual screening of novel peptide molecules targeting specific biological functions from a wide variety of natural resources.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

Adipose-Tumor Crosstalk contributes to CXCL5 Mediated Immune Evasion in PDAC

Background

CXCR1/2 inhibitors are being implemented with immunotherapies in PDAC clinical trials. Cytokines responsible for stimulating these receptors include CXCL ligands, typically secreted by activated immune cells, fibroblasts, and even adipocytes. Obesity has been linked to poor patient outcome and altered anti-tumor immunity. Adipose-derived cytokines and chemokines have been implicated as potential drivers of tumor cell immune evasion, suggesting a possibility of susceptibility to targeting specifically in the context of obesity.

Methods

RNA-sequencing of human PDAC cell lines was used to assess differential influences on the cancer cell transcriptome after treatment with conditioned media from peri-pancreatic adipose tissue of lean and obese PDAC patients. The adipose-induced secretome of PDAC cells was then assessed by cytokine arrays and ELISAs. Lentiviral transduction and CRISPR-Cas9 was used to knock out CXCL5 from a murine PDAC cell line for orthotopic tumor studies in diet-induced obese, syngeneic mice. Flow cytometry was used to define the immune profiles of tumors. Anti-PD-1 immune checkpoint blockade therapy was administered to alleviate T cell exhaustion and invoke an immune response, while the mice were monitored at endpoint for differences in tumor size.

Results

The chemokine CXCL5 was secreted in response to stimulation of PDAC cells with human adipose conditioned media (hAT-CM). PDAC CXCL5 secretion was induced by either IL-1β or TNF, but neutralization of both was required to limit secretion. Ablation of CXCL5 from tumors promoted an immune phenotype susceptible to PD-1 inhibitor therapy. While application of anti-PD-1 treatment to control tumors failed to alter tumor growth, knockout CXCL5 tumors were diminished.

Conclusions

In summary, our findings show that known adipokines TNF and IL-1β can stimulate CXCL5 release from PDAC cells in vitro. In vivo , CXCL5 depletion alone is sufficient to promote T cell infiltration into tumors in an obese setting, but requires checkpoint blockade inhibition to alleviate tumor burden.

DATA AVAILABILITY STATEMENT

Raw and processed RNAseq data will be further described in the GEO accession database ( awaiting approval from GEO for PRJ number ). Additional raw data is included in the supplemental material and available upon reasonable request.

WHAT IS ALREADY KNOWN ON THIS TOPIC

Obesity is linked to a worsened patient outcome and immunogenic tumor profile in PDAC. CXCR1/2 inhibitors have begun to be implemented in combination with immune checkpoint blockade therapies to promote T cell infiltration under the premise of targeting the myeloid rich TME.

WHAT THIS STUDY ADDS

Using in vitro/ex vivo cell and tissue culture-based assays with in vivo mouse models we have identified that adipose derived IL-1β and TNF can promote tumor secretion of CXCL5 which acts as a critical deterrent to CD8 T cell tumor infiltration, but loss of CXCL5 also leads to a more immune suppressive myeloid profile.

HOW THIS STUDY MIGHT AFFECT RESEARCH PRACTICE OR POLICY

This study highlights a mechanism and emphasizes the efficacy of single CXCR1/2 ligand targeting that could be beneficial to overcoming tumor immune-evasion even in the obese PDAC patient population.

PrePPI: A Structure Informed Proteome-wide Database of Protein-Protein Interactions

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.

Recent advances in predicting and modeling protein-protein interactions

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

PrePPI: A structure informed proteome-wide database of protein-protein interactions

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural clues within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) clue is derived from templatebased modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ~1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features ( https://honiglab.c2b2.columbia.edu/PrePPI ). PrePPI is a state-of- the-art resource that offers an unprecedented structure-informed view of the human interactome.

Graphic Abstract

In Silico Examination of Single Nucleotide Missense Mutations in NHLH2, a Gene Linked to Infertility and Obesity

Continual advances in our understanding of the human genome have led to exponential increases in known single nucleotide variants. The characterization of each of the variants lags behind. For researchers needing to study a single gene, or multiple genes in a pathway, there must be ways to narrow down pathogenic variants from those that are silent or pose less pathogenicity. In this study, we use the NHLH2 gene which encodes the nescient helix-loop-helix 2 (Nhlh2) transcription factor in a systematic analysis of all missense mutations to date in the gene. The NHLH2 gene was first described in 1992. Knockout mice created in 1997 indicated a role for this protein in body weight control, puberty, and fertility, as well as the motivation for sex and exercise. Only recently have human carriers of NHLH2 missense variants been characterized. Over 300 missense variants for the NHLH2 gene are listed in the NCBI single nucleotide polymorphism database (dbSNP). Using in silico tools, predicted pathogenicity of the variants narrowed the missense variants to 37 which were predicted to affect NHLH2 function. These 37 variants cluster around the basic-helix-loop-helix and DNA binding domains of the transcription factor, and further analysis using in silico tools provided 21 SNV resulting in 22 amino acid changes for future wet lab analysis. The tools used, findings, and predictions for the variants are discussed considering the known function of the NHLH2 transcription factor. Overall use of these in silico tools and analysis of these data contribute to our knowledge of a protein which is both involved in the human genetic syndrome, Prader-Willi syndrome, and in controlling genes involved in body weight control, fertility, puberty, and behavior in the general population, and may provide a systematic methodology for others to characterize variants for their gene of interest.

Recent advances in metagenomic analysis of different ecological niches for enhanced biodegradation of recalcitrant lignocellulosic biomass

Lignocellulose wastes stemming from agricultural residues can offer an excellent opportunity as alternative energy solutions in addition to fossil fuels. Besides, the unrestrained burning of agricultural residues can lead to the destruction of the soil microflora and associated soil sterilization. However, the difficulties associated with the biodegradation of lignocellulose biomasses remain as a formidable challenge for their sustainable management. In this respect, metagenomics can be used as an effective option to resolve such dilemma because of its potential as the next generation sequencing technology and bioinformatics tools to harness novel microbial consortia from diverse environments (e.g., soil, alpine forests, and hypersaline/acidic/hot sulfur springs). In light of the challenges associated with the bulk-scale biodegradation of lignocellulose-rich agricultural residues, this review is organized to help delineate the fundamental aspects of metagenomics towards the assessment of the microbial consortia and novel molecules (such as biocatalysts) which are otherwise unidentifiable by conventional laboratory culturing techniques. The discussion is extended further to highlight the recent advancements (e.g., from 2011 to 2022) in metagenomic approaches for the isolation and purification of lignocellulolytic microbes from different ecosystems along with the technical challenges and prospects associated with their wide implementation and scale-up. This review should thus be one of the first comprehensive reports on the metagenomics-based analysis of different environmental samples for the isolation and purification of lignocellulose degrading enzymes.