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Epithelial-to-mesenchymal transition (EMT) is associated with tumor initiation, metastasis, and drug resistance. However, the mechanisms underlying these associations are largely unknown. We studied several tumor types to identify the source of EMT gene expression signals and a potential mechanism of resistance to immuno-oncology treatment. Across tumor types, EMT-related gene expression was strongly associated with expression of stroma-related genes. Based on RNA sequencing of multiple patient-derived xenograft models, EMT-related gene expression was enriched in the stroma versus parenchyma. EMT-related markers were predominantly expressed by cancer-associated fibroblasts (CAFs), cells of mesenchymal origin which produce a variety of matrix proteins and growth factors. Scores derived from a 3-gene CAF transcriptional signature ( COL1A1 , COL1A2 , COL3A1 ) were sufficient to reproduce association between EMT-related markers and disease prognosis. Our results suggest that CAFs are the primary source of EMT signaling and have potential roles as biomarkers and targets for immuno-oncology therapies.

Some small cell lung cancers (SCLCs) are highly sensitive to inhibitors of the histone demethylase LSD1. LSD1 inhibitors are thought to induce their anti-proliferative effects by blocking neuroendocrine differentiation, but the mechanisms by which LSD1 controls the SCLC neuroendocrine phenotype are not well understood. To identify genes required for LSD1 inhibitor sensitivity in SCLC, we performed a positive selection genome-wide CRISPR/Cas9 loss of function screen and found that ZFP36L1 , an mRNA-binding protein that destabilizes mRNAs, is required for LSD1 inhibitor sensitivity. LSD1 binds and represses ZFP36L1 and upon LSD1 inhibition, ZFP36L1 expression is restored, which is sufficient to block the SCLC neuroendocrine differentiation phenotype and induce a non-neuroendocrine “inflammatory” phenotype. Mechanistically, ZFP36L1 binds and destabilizes SOX2 and INSM1 mRNAs, two transcription factors that are required for SCLC neuroendocrine differentiation. This work identifies ZFP36L1 as an LSD1 target gene that controls the SCLC neuroendocrine phenotype and demonstrates that modulating mRNA stability of lineage transcription factors controls neuroendocrine to non-neuroendocrine plasticity. LSD1 inhibition blocks the neuroendocrine phenotype of some small cell lung cancers (SCLCs). Here, a genome-wide CRISPR/Cas9 LSD1 inhibitor resistance screen identifies the mRNA-binding protein ZFP36L1 as a gene repressed by LSD1 that when restored inhibits SCLC neuroendocrine differentiation.

Evolve and resequencing (E&R) was applied to lab adaptation of Toxoplasma gondii for over 1,500 generations with the goal of mapping host-independent in vitro virulence traits. Phenotypic assessments of steps across the lytic cycle revealed that only traits needed in the extracellular milieu evolved. Nonsynonymous single-nucleotide polymorphisms (SNPs) in only one gene, a P4 flippase, fixated across two different evolving populations, whereas dramatic changes in the transcriptional signature of extracellular parasites were identified. Newly developed computational tools correlated phenotypes evolving at different rates with specific transcriptomic changes. A set of 300 phenotype-associated genes was mapped, of which nearly 50% is annotated as hypothetical. Validation of a select number of genes by knockouts confirmed their role in lab adaptation and highlights novel mechanisms underlying in vitro virulence traits. Further analyses of differentially expressed genes revealed the development of a “pro-tachyzoite” profile as well as the upregulation of the fatty acid biosynthesis (FASII) pathway. The latter aligned with the P4 flippase SNP and aligned with a low abundance of medium-chain fatty acids at low passage, indicating this is a limiting factor in extracellular parasites. In addition, partial overlap with the bradyzoite differentiation transcriptome in extracellular parasites indicated that stress pathways are involved in both situations. This was reflected in the partial overlap between the assembled ApiAP2 and Myb transcription factor network underlying the adapting extracellular state with the bradyzoite differentiation program. Overall, E&R is a new genomic tool successfully applied to map the development of polygenic traits underlying in vitro virulence of T. gondii. IMPORTANCE It has been well established that prolonged in vitro cultivation of Toxoplasma gondii augments progression of the lytic cycle. This lab adaptation results in increased capacities to divide, migrate, and survive outside a host cell, all of which are considered host-independent virulence factors. However, the mechanistic basis underlying these enhanced virulence features is unknown. Here, E&R was utilized to empirically characterize the phenotypic, genomic, and transcriptomic changes in the non-lab-adapted strain, GT1, during 2.5 years of lab adaptation. This identified the shutdown of stage differentiation and upregulation of lipid biosynthetic pathways as the key processes being modulated. Furthermore, lab adaptation was primarily driven by transcriptional reprogramming, which rejected the starting hypothesis that genetic mutations would drive lab adaptation. Overall, the work empirically shows that lab adaptation augments T. gondii’s in vitro virulence by transcriptional reprogramming and that E&R is a powerful new tool to map multigenic traits.

Human cancers often re-express germline factors, yet their mechanistic role in oncogenesis and cancer progression remains unknown. Here we demonstrate that DEAD-box helicase 4 (DDX4), a germline factor and RNA helicase conserved in all multicellular organisms, contributes to increased cell motility and cisplatin-mediated drug resistance in small cell lung cancer (SCLC) cells. Proteomic analysis suggests that DDX4 expression upregulates proteins related to DNA repair and immune/inflammatory response. Consistent with these trends in cell lines, DDX4 depletion compromised in vivo tumor development while its overexpression enhanced tumor growth even after cisplatin treatment in nude mice. Further, the relatively higher DDX4 expression in SCLC patients correlates with decreased survival and shows increased expression of immune/inflammatory response markers. Taken together, we propose that DDX4 increases SCLC cell survival, by increasing the DNA damage and immune response pathways, especially under challenging conditions such as cisplatin treatment. DDX4, a conserved germline factor and RNA helicase, increases small cell lung cancer cell survival by regulating DNA damage and immune response pathways and contributes to cisplatin-mediated drug resistance.

BackgroundThe phase 2 KEYVIBE-002 study (NCT04725188) investigated the coformulation of the TIGIT antibody vibostolimab with the anti-PD-1 antibody pembrolizumab (vibo/pembro) with or without docetaxel in mNSCLC previously treated with anti-PD-(L)1 therapy and platinum chemotherapy. No significant difference in PFS was observed between vibo/pembro with or without docetaxel versus docetaxel alone. We present an exploratory biomarker analysis examining PD-L1 tumor proportion score (TPS; as a continuous score and ≥1% vs <1%) and TIGIT immunohistochemical (IHC) level (≥median vs 0. bThe default method to calculate the Cl of time-to-event data is log-log.

It has been well established that prolonged in vitro cultivation of Toxoplasma gondii augments progression of the lytic cycle. This lab adaptation results in increased capacities to divide, migrate, and survive outside a host cell, all of which are considered host-independent virulence factors. Evolve and resequencing (E&R) was applied to lab adaptation of Toxoplasma gondii for over 1,500 generations with the goal of mapping host-independent in vitro virulence traits. Phenotypic assessments of steps across the lytic cycle revealed that only traits needed in the extracellular milieu evolved. Nonsynonymous single-nucleotide polymorphisms (SNPs) in only one gene, a P4 flippase, fixated across two different evolving populations, whereas dramatic changes in the transcriptional signature of extracellular parasites were identified. Newly developed computational tools correlated phenotypes evolving at different rates with specific transcriptomic changes. A set of 300 phenotype-associated genes was mapped, of which nearly 50% is annotated as hypothetical. Validation of a select number of genes by knockouts confirmed their role in lab adaptation and highlights novel mechanisms underlying in vitro virulence traits. Further analyses of differentially expressed genes revealed the development of a “pro-tachyzoite” profile as well as the upregulation of the fatty acid biosynthesis (FASII) pathway. The latter aligned with the P4 flippase SNP and aligned with a low abundance of medium-chain fatty acids at low passage, indicating this is a limiting factor in extracellular parasites. In addition, partial overlap with the bradyzoite differentiation transcriptome in extracellular parasites indicated that stress pathways are involved in both situations. This was reflected in the partial overlap between the assembled ApiAP2 and Myb transcription factor network underlying the adapting extracellular state with the bradyzoite differentiation program. Overall, E&R is a new genomic tool successfully applied to map the development of polygenic traits underlying in vitro virulence of T. gondii . IMPORTANCE It has been well established that prolonged in vitro cultivation of Toxoplasma gondii augments progression of the lytic cycle. This lab adaptation results in increased capacities to divide, migrate, and survive outside a host cell, all of which are considered host-independent virulence factors. However, the mechanistic basis underlying these enhanced virulence features is unknown. Here, E&R was utilized to empirically characterize the phenotypic, genomic, and transcriptomic changes in the non-lab-adapted strain, GT1, during 2.5 years of lab adaptation. This identified the shutdown of stage differentiation and upregulation of lipid biosynthetic pathways as the key processes being modulated. Furthermore, lab adaptation was primarily driven by transcriptional reprogramming, which rejected the starting hypothesis that genetic mutations would drive lab adaptation. Overall, the work empirically shows that lab adaptation augments T. gondii ’s in vitro virulence by transcriptional reprogramming and that E&R is a powerful new tool to map multigenic traits.

Human cancers often re-express germline factors, yet their mechanistic role in oncogenesis and cancer progression remains unknown. Here we demonstrate that DDX4, a germline factor and RNA helicase conserved in all multicellular organisms, contributes to epithelial mesenchyme transition (EMT)-like features and cisplatin resistance in small cell lung cancer (SCLC) cells. DDX4 depletion in H69AR and SHP77 cell lines decreased motility and resistance to cisplatin, whereas its overexpression increased these features. Proteomic analysis suggests that DDX4 upregulates metabolic protein expression related to DNA repair and immune/inflammatory response, suggesting its fundamental function may be in regulating cellular metabolism. Consistent with these trends in cell lines, DDX4 depletion compromised in vivo tumor development while its overexpression enhanced tumor growth even after cisplatin treatment in nude mice. Although the DDX4 expression level in somatic tumors is generally low compared to that in the germline, the relatively higher DDX4 expression in SCLC patients correlates with decreased survival and shows increased expression of EMT and cisplatin resistance markers. Taken together, we conclude that DDX4 influences the survival of SCLC patients by altering cellular metabolism in response to environmental cues such as drug treatments. This fundamental function of DDX4 as a germline factor might be applicable in other cancer types that express DDX4 and may serve as a key to combat specific tumors that are highly resistant to treatments. Competing Interest Statement The authors have declared no competing interest.

Every organism contains a few hundred to thousands of proteins. A protein is made of a sequence of molecular building blocks named amino acids. Amino acids will be referred to as residues. Every protein performs one or more functions in the cell. In order for a protein to do its job, it requires to bind properly to other partner proteins. Many genetic diseases such as cancer are caused by mutations (changes) of specific residues which cause disturbances in the functions of those proteins. The problem of prediction of protein binding site is a crucial topic in computational biology. A protein is usually made up of 50 to a few thousand residues. A contact site can occur within a protein or with other proteins. By having a robust and accurate model for identifying residues that are involved in the binding site, scientists can investigate the impact of critical mutations and residues that can cause genetic diseases. The main focus of this thesis is to propose a machine learning model for predicting the binding site between two proteins. By extracting structural information from a protein, we can have additional knowledge of binding sites. This structural information can be converted into a penalty matrix for a graphical model to be learned from the protein sequence. The second part of this thesis is mostly focused on motion planning algorithms for proteins and simulation of the protein pathway changes using a Monte Carlo based method. Later, by applying a novel geometry based scoring function, we cluster the intermediate conformations into corresponding subsets that may indicate interesting intermediate states.

Over the past decade there have been impressive advances in determining the 3D structures of protein complexes. However, there are still many complexes with unknown structures, even when the structures of the individual proteins are known. The advent of protein sequence information provides an opportunity to leverage evolutionary information to enhance the accuracy of protein-protein interface prediction. To this end, several statistical and machine learning methods have been proposed. In particular, direct coupling analysis has recently emerged as a promising approach for identification of protein contact maps from sequential information. However, the ability of these methods to detect protein-protein inter-residue contacts remains relatively limited. In this work, we propose a method to integrate sequential and co-evolution information with structural and functional information to increase the performance of protein-protein interface prediction. Further, we present a post-processing clustering method that improves the average relative F1 score by 70 % and 24 % and the precision by 80 % and 36 % in comparison with two state-of-the-art methods PSICOV and GREMLIN.

Gait recognition is the characterization of unique biometric patterns associated with each individual which can be utilized to identify a person without direct contact. A public gait database with a relatively large number of subjects can provide a great opportunity for future studies to build and validate gait authentication models. The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two endpoints (320 meters) during two different sessions and record their gait data using two smartphones, one attached to the right thigh and another one on the left side of the waist. This data is collected to be utilized by a deep learning-based method that requires enough time points. The metadata including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available.