Dissecting the treatment-naive ecosystem of human melanoma brain metastasis.

Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma; yet, our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naive MBMs and 10 extracranial melanoma metastases (ECMs) and matched spatial single-cell transcriptomics and T cell receptor (TCR)-seq. Cancer cells from MBM were more chromosomally unstable, adopted a neuronal-like cell state, and enriched for spatially variably expressed metabolic pathways. Key observations were validated in independent patient cohorts, patient-derived MBM/ECM xenograft models, RNA/ATAC-seq, proteomics, and multiplexed imaging. Integrated spatial analyses revealed distinct geography of putative cancer immune evasion and evidence for more abundant intra-tumoral B to plasma cell differentiation in lymphoid aggregates in MBM. MBM harbored larger fractions of monocyte-derived macrophages and dysfunctional TOX+CD8+ T cells with distinct expression of immune checkpoints. This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration.

A modular master regulator landscape controls cancer transcriptional identity.

Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.

A molecular single-cell lung atlas of lethal COVID-19.

Respiratory failure is the leading cause of death in patients with severe SARS-CoV-2 infection1,2, but the host response at the lung tissue level is poorly understood. Here we performed single-nucleus RNA sequencing of about 116,000 nuclei from the lungs of nineteen individuals who died of COVID-19 and underwent rapid autopsy and seven control individuals. Integrated analyses identified substantial alterations in cellular composition, transcriptional cell states, and cell-to-cell interactions, thereby providing insight into the biology of lethal COVID-19. The lungs from individuals with COVID-19 were highly inflamed, with dense infiltration of aberrantly activated monocyte-derived macrophages and alveolar macrophages, but had impaired T cell responses. Monocyte/macrophage-derived interleukin-1ß and epithelial cell-derived interleukin-6 were unique features of SARS-CoV-2 infection compared to other viral and bacterial causes of pneumonia. Alveolar type 2 cells adopted an inflammation-associated transient progenitor cell state and failed to undergo full transition into alveolar type 1 cells, resulting in impaired lung regeneration. Furthermore, we identified expansion of recently described CTHRC1+ pathological fibroblasts3 contributing to rapidly ensuing pulmonary fibrosis in COVID-19. Inference of protein activity and ligand-receptor interactions identified putative drug targets to disrupt deleterious circuits. This atlas enables the dissection of lethal COVID-19, may inform our understanding of long-term complications of COVID-19 survivors, and provides an important resource for therapeutic development.

ProtFus: A Comprehensive Method Characterizing Protein-Protein Interactions of Fusion Proteins.

Tailored therapy aims to cure cancer patients effectively and safely, based on the complex interactions between patients' genomic features, disease pathology and drug metabolism. Thus, the continual increase in scientific literature drives the need for efficient methods of data mining to improve the extraction of useful information from texts based on patients' genomic features. An important application of text mining to tailored therapy in cancer encompasses the use of mutations and cancer fusion genes as moieties that change patients' cellular networks to develop cancer, and also affect drug metabolism. Fusion proteins, which are derived from the slippage of two parental genes, are produced in cancer by chromosomal aberrations and trans-splicing. Given that the two parental proteins for predicted fusion proteins are known, we used our previously developed method for identifying chimeric protein-protein interactions (ChiPPIs) associated with the fusion proteins. Here, we present a validation approach that receives fusion proteins of interest, predicts their cellular network alterations by ChiPPI and validates them by our new method, ProtFus, using an online literature search. This process resulted in a set of 358 fusion proteins and their corresponding protein interactions, as a training set for a Naïve Bayes classifier, to identify predicted fusion proteins that have reliable evidence in the literature and that were confirmed experimentally. Next, for a test group of 1817 fusion proteins, we were able to identify from the literature 2908 PPIs in total, across 18 cancer types. The described method, ProtFus, can be used for screening the literature to identify unique cases of fusion proteins and their PPIs, as means of studying alterations of protein networks in cancers. Availability: http://protfus.md.biu.ac.il/.

ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer.

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein-protein interactions (ChiPPI) that uses the domain-domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF ß), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer.

ChiTaRS-3.1-the enhanced chimeric transcripts and RNA-seq database matched with protein-protein interactions.

Discovery of chimeric RNAs, which are produced by chromosomal translocations as well as the joining of exons from different genes by trans-splicing, has added a new level of complexity to our study and understanding of the transcriptome. The enhanced ChiTaRS-3.1 database (http://chitars.md.biu.ac.il) is designed to make widely accessible a wealth of mined data on chimeric RNAs, with easy-to-use analytical tools built-in. The database comprises 34 922: chimeric transcripts along with 11 714: cancer breakpoints. In this latest version, we have included multiple cross-references to GeneCards, iHop, PubMed, NCBI, Ensembl, OMIM, RefSeq and the Mitelman collection for every entry in the 'Full Collection'. In addition, for every chimera, we have added a predicted Chimeric Protein-Protein Interaction (ChiPPI) network, which allows for easy visualization of protein partners of both parental and fusion proteins for all human chimeras. The database contains a comprehensive annotation for 34 922: chimeric transcripts from eight organisms, and includes the manual annotation of 200 sense-antiSense (SaS) chimeras. The current improvements in the content and functionality to the ChiTaRS database make it a central resource for the study of chimeric transcripts and fusion proteins.

Loss of Pip4k2c confers liver-metastatic organotropism through insulin-dependent PI3K-AKT pathway activation.

Liver metastasis (LM) confers poor survival and therapy resistance across cancer types, but the mechanisms of liver-metastatic organotropism remain unknown. Here, through in vivo CRISPR-Cas9 screens, we found that Pip4k2c loss conferred LM but had no impact on lung metastasis or primary tumor growth. Pip4k2c-deficient cells were hypersensitized to insulin-mediated PI3K/AKT signaling and exploited the insulin-rich liver milieu for organ-specific metastasis. We observed concordant changes in PIP4K2C expression and distinct metabolic changes in 3,511 patient melanomas, including primary tumors, LMs and lung metastases. We found that systemic PI3K inhibition exacerbated LM burden in mice injected with Pip4k2c-deficient cancer cells through host-mediated increase in hepatic insulin levels; however, this circuit could be broken by concurrent administration of an SGLT2 inhibitor or feeding of a ketogenic diet. Thus, this work demonstrates a rare example of metastatic organotropism through co-optation of physiological metabolic cues and proposes therapeutic avenues to counteract these mechanisms.

Systematic Pan-cancer Functional Inference and Validation of Hyper, Hypo and Neomorphic Mutations.

While the functional effects of many recurrent cancer mutations have been characterized, the TCGA repository comprises more than 10M non-recurrent events, whose function is unknown. We propose that the context specific activity of transcription factor (TF) proteins-as measured by expression of their transcriptional targets-provides a sensitive and accurate reporter assay to assess the functional role of oncoprotein mutations. Analysis of differentially active TFs in samples harboring mutations of unknown significance-compared to established gain (GOF/hypermorph) or loss (LOF/hypomorph) of function-helped functionally characterize 577,866 individual mutational events across TCGA cohorts, including identification of mutations that are either neomorphic (gain of novel function) or phenocopy other mutations ( mutational mimicry ). Validation using mutation knock-in assays confirmed 15 out of 15 predicted gain and loss of function mutations and 15 of 20 predicted neomorphic mutations. This could help determine targeted therapy in patients with mutations of unknown significance in established oncoproteins.

KLRG1 marks tumor-infiltrating CD4 T cell subsets associated with tumor progression and immunotherapy response.

Current methods for biomarker discovery and target identification in immuno-oncology rely on static snapshots of tumor immunity. To thoroughly characterize the temporal nature of antitumor immune responses, we developed a 34-parameter spectral flow cytometry panel and performed high-throughput analyses in critical contexts. We leveraged two distinct preclinical models that recapitulate cancer immunoediting (NPK-C1) and immune checkpoint blockade (ICB) response (MC38), respectively, and profiled multiple relevant tissues at and around key inflection points of immune surveillance and escape and/or ICB response. Machine learning-driven data analysis revealed a pattern of KLRG1 expression that uniquely identified intratumoral effector CD4 T cell populations that constitutively associate with tumor burden across tumor models, and are lost in tumors undergoing regression in response to ICB. Similarly, a Helios-KLRG1+ subset of tumor-infiltrating regulatory T cells was associated with tumor progression from immune equilibrium to escape and was also lost in tumors responding to ICB. Validation studies confirmed KLRG1 signatures in human tumor-infiltrating CD4 T cells associate with disease progression in renal cancer. These findings nominate KLRG1+ CD4 T cell populations as subsets for further investigation in cancer immunity and demonstrate the utility of longitudinal spectral flow profiling as an engine of dynamic biomarker discovery.

Multimodal single-cell and whole-genome sequencing of small, frozen clinical specimens.

Single-cell genomics enables dissection of tumor heterogeneity and molecular underpinnings of drug response at an unprecedented resolution1-11. However, broad clinical application of these methods remains challenging, due to several practical and preanalytical challenges that are incompatible with typical clinical care workflows, namely the need for relatively large, fresh tissue inputs. In the present study, we show that multimodal, single-nucleus (sn)RNA/T cell receptor (TCR) sequencing, spatial transcriptomics and whole-genome sequencing (WGS) are feasible from small, frozen tissues that approximate routinely collected clinical specimens (for example, core needle biopsies). Compared with data from sample-matched fresh tissue, we find a similar quality in the biological outputs of snRNA/TCR-seq data, while reducing artifactual signals and compositional biases introduced by fresh tissue processing. Profiling sequentially collected melanoma samples from a patient treated in the KEYNOTE-001 trial12, we resolved cellular, genomic, spatial and clonotype dynamics that represent molecular patterns of heterogeneous intralesional evolution during anti-programmed cell death protein 1 therapy. To demonstrate applicability to banked biospecimens of rare diseases13, we generated a single-cell atlas of uveal melanoma liver metastasis with matched WGS data. These results show that single-cell genomics from archival, clinical specimens is feasible and provides a framework for translating these methods more broadly to the clinical arena.

tRNA methylation resolves codon usage bias at the limit of cell viability.

Codon usage of each genome is closely correlated with the abundance of tRNA isoacceptors. How codon usage bias is resolved by tRNA post-transcriptional modifications is largely unknown. Here we demonstrate that the N1-methylation of guanosine at position 37 (m1G37) on the 3'-side of the anticodon, while not directly responsible for reading of codons, is a neutralizer that resolves differential decoding of proline codons. A genome-wide suppressor screen of a non-viable Escherichia coli strain, lacking m1G37, identifies proS suppressor mutations, indicating a coupling of methylation with tRNA prolyl-aminoacylation that sets the limit of cell viability. Using these suppressors, where prolyl-aminoacylation is decoupled from tRNA methylation, we show that m1G37 neutralizes differential translation of proline codons by the major isoacceptor. Lack of m1G37 inactivates this neutralization and exposes the need for a minor isoacceptor for cell viability. This work has medical implications for bacterial species that exclusively use the major isoacceptor for survival.

A multi-organ chip with matured tissue niches linked by vascular flow.

Engineered tissues can be used to model human pathophysiology and test the efficacy and safety of drugs. Yet, to model whole-body physiology and systemic diseases, engineered tissues with preserved phenotypes need to physiologically communicate. Here we report the development and applicability of a tissue-chip system in which matured human heart, liver, bone and skin tissue niches are linked by recirculating vascular flow to allow for the recapitulation of interdependent organ functions. Each tissue is cultured in its own optimized environment and is separated from the common vascular flow by a selectively permeable endothelial barrier. The interlinked tissues maintained their molecular, structural and functional phenotypes over 4 weeks of culture, recapitulated the pharmacokinetic and pharmacodynamic profiles of doxorubicin in humans, allowed for the identification of early miRNA biomarkers of cardiotoxicity, and increased the predictive values of clinically observed miRNA responses relative to tissues cultured in isolation and to fluidically interlinked tissues in the absence of endothelial barriers. Vascularly linked and phenotypically stable matured human tissues may facilitate the clinical applicability of tissue chips.

Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses.

Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.

ASAPP: Architectural Similarity-Based Automated Pathway Prediction System and Its Application in Host-Pathogen Interactions.

The significance of metabolic pathway prediction is to envision the viable unknown transformations that can occur provided the appropriate enzymes are present. It can facilitate the prediction of the consequences of host-pathogen interactions. In this article, we have proposed a new algorithm Architectural Similarity-based Automated Pathway Prediction (ASAPP) to predict metabolic pathways based on the structural similarity among the metabolites. ASAPP takes two-dimensional structure and molecular weight of metabolites as input, and generates a list of probable transformations without the knowledge of any externally established reactions, with an accuracy of 85.09 percent. ASAPP has also been applied to predict the outcome of pathogen liberated toxins on the carbohydrate and lipid pathways of the hosts. We have analyzed the disruption of host pathways in the presence of toxins, and have found that some metabolites in Glycolysis and the TCA cycle have a high chance of being the breakpoints in the pathway. The tool is available at http://asapp.droppages.com/.

Design principles of gene evolution for niche adaptation through changes in protein-protein interaction networks.

In contrast to fossorial and above-ground organisms, subterranean species have adapted to the extreme stresses of living underground. We analyzed the predicted protein-protein interactions (PPIs) of all gene products, including those of stress-response genes, among nine subterranean, ten fossorial, and 13 aboveground species. We considered 10,314 unique orthologous protein families and constructed 5,879,879 PPIs in all organisms using ChiPPI. We found strong association between PPI network modulation and adaptation to specific habitats, noting that mutations in genes and changes in protein sequences were not linked directly with niche adaptation in the organisms sampled. Thus, orthologous hypoxia, heat-shock, and circadian clock proteins were found to cluster according to habitat, based on PPIs rather than on sequence similarities. Curiously, "ordered" domains were preserved in aboveground species, while "disordered" domains were conserved in subterranean organisms, and confirmed for proteins in DistProt database. Furthermore, proteins with disordered regions were found to adopt significantly less optimal codon usage in subterranean species than in fossorial and above-ground species. These findings reveal design principles of protein networks by means of alterations in protein domains, thus providing insight into deep mechanisms of evolutionary adaptation, generally, and particularly of species to underground living and other confined habitats.

Cluster Quality based Non-Reductional (CQNR) oversampling technique and effector protein predictor based on 3D structure (EPP3D) of proteins.

BACKGROUND: Effector proteins of bacteria infect their hosts by specific dedicated machinery identified as secretion systems. Currently, no mechanism to identify the effector proteins based on their 3D structure has been reported in the literature. In order to identify effector proteins, extraction of features from their 3D structure is crucial. However, effector protein datasets are highly imbalanced. State-of-the-art oversampling algorithms are incapable of dealing with such datasets. They usually eliminate samples as noise. They do not ensure generation of synthetic samples strictly in the vicinity of the minority class samples. In effector protein datasets, deletion of any samples as noise would lead to loss of crucial information. Furthermore, generation of synthetic samples of the minority class in the vicinity of majority class samples would lead to an inept classifier. METHOD: In this paper, we introduce an algorithm called Cluster Quality based Non-Reductional (CQNR) oversampling technique. Its novelty lies in generating new samples proportional to the distribution of samples of the minority classes, without eliminating any sample as noise. Utilizing CQNR, we develop a novel Effector Protein Predictor based on the 3D (EPP3D) structure of proteins. EPP3D is trained on a feature set, balanced by CQNR, comprising 3D structure-based features, namely, convex hull layer count, surface atom composition, radius of gyration, packing density and compactness, derived from the 3D structure of the experimentally verified effector proteins. RESULT: Fscore and Gmean demonstrate that CQNR has outperformed some well-established oversampling methods by approximately 3-5%, with respect to classification accuracy, on five benchmark datasets and three other highly imbalanced synthetically generated datasets. Likewise, for classification of pathogenic effector proteins, a significant improvement of 7-9% in accuracy has been noticed, on the application of CQNR followed by EPP3D. Moreover, EPP3D has exhibited an improvement of 2-4% in classifying effector proteins based on their 3D structure compared to the classification of effector proteins based on their amino acid sequences. The software for CQNR and EPP3D are available at http://projectphd.droppages.com/CQNR.html.

Species scaling and extrapolation.

The various scaling methodologies and molecular features analysis were applied to new dataset to predict human pharmacokinetics studies. Whereas the predictive accuracies demonstrated across all of the various methodologies were lower for this higher clearance compound dataset, scaling from species continued to be an accurate methodology, and human volume of distribution was similarly well predicted regardless of scaling methodology. Also, extrapolation is the method for constructing new data points given a set of discrete data points. Methods estimate is reasonably reliable for short times, but for longer times, the estimate is liable to become less accurate. Species Scaling and Extrapolation are useful for acquiring toxicological data- epidemiological and experimental study. Animal studies help us to understand toxicity characteristics of a chemical before human exposure is allowed, whereas the epidemiological method generally does not. Species scaling and extrapolation from animals is necessary in many cases which helps in dealing with the so-called human risks more properly.

Epigenomics.

'Epigenomics' can be termed as the study of the effects of chromatin structure, including the higher order of chromatin folding and attachment to the nuclear matrix, packaging of DNA around nucleosomes, covalent modifications of histone tails and DNA methylation. This has evolved to include any process that alters gene activity without changing the DNA sequence, and leads to modifications that can be transmitted to daughter cells. It also leads to a better knowledge of the changes in the regulation of genes and genomes that occur in major psychosis. It may also aid in understanding why the same gene sequence may predispose an individual to schizophrenia or bipolar disorder and in other cases does not, and elucidate the molecular mechanisms of how harmful; environmental factors interact with the genome. Results from the work may further lead to new diagnostics and effective therapies.

Blood stage parasites: sufficient to induce protective immunity.

Merozoites are the surface antigens and variant antigens expressed on the surface of malaria-infected erythrocytes (including PfEMP1) are both targets of protective antibody responses. The mechanism of the modified immune response was observed after subpatent infections. Subpatently infected mice had increased antigen-specific T-cell responses; they were not better protected than patently infected mice. The study of human volunteers, the absence of detectable malaria-specific antibodies probably reflects the extremely low parasite doses used for immunization. Induction of this type of immunity by immunizing with low doses of purified antigens from whole parasites may be an alternative but highly effective vaccine strategy.