Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody-drug conjugates and immunotherapeutic agents.

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens.

Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action. We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity. This revealed an unappreciated positive association between mitochondrial E3 ubiquitin-protein ligase MARCH5 dependency and sensitivity to MCL1 inhibitors in breast cancer cell lines. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic data sets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.

Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks.

BACKGROUND: Inference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature. The adoption and extension of such methods in the academic community has been hampered by the lack of freely available, efficient algorithms and an accompanying demonstration of their applicability using current public networks. RESULTS: In this article, we propose a new statistical method that will infer likely upstream regulators based on observed patterns of up- and down-regulated transcripts. The method is suitable for use with public interaction networks with a mix of signed and unsigned causal edges. It subsumes and extends two previously published approaches and we provide a novel algorithmic method for efficient statistical inference. Notably, we demonstrate the feasibility of using the approach to generate biological insights given current public networks in the context of controlled in-vitro overexpression experiments, stem-cell differentiation data and animal disease models. We also provide an efficient implementation of our method in the R package QuaternaryProd available to download from Bioconductor. CONCLUSIONS: In this work, we have closed an important gap in utilizing causal networks to analyze differentially expressed genes. Our proposed Quaternary test statistic incorporates all available evidence on the potential relevance of an upstream regulator. The new approach broadens the use of these types of statistics for highly curated signed networks in which ambiguities arise but also enables the use of networks with unsigned edges. We design and implement a novel computational method that can efficiently estimate p-values for upstream regulators in current biological settings. We demonstrate the ready applicability of the implemented method to analyze differentially expressed genes using the publicly available networks.

Molecular causes of transcriptional response: a Bayesian prior knowledge approach.

MOTIVATION: The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes. METHODS: We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile. RESULTS: Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated. AVAILABILITY AND IMPLEMENTATION: Source code for the method is available upon request.

Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors.

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell 'avatars' capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.

Causal reasoning on biological networks: interpreting transcriptional changes.

MOTIVATION: The interpretation of high-throughput datasets has remained one of the central challenges of computational biology over the past decade. Furthermore, as the amount of biological knowledge increases, it becomes more and more difficult to integrate this large body of knowledge in a meaningful manner. In this article, we propose a particular solution to both of these challenges. METHODS: We integrate available biological knowledge by constructing a network of molecular interactions of a specific kind: causal interactions. The resulting causal graph can be queried to suggest molecular hypotheses that explain the variations observed in a high-throughput gene expression experiment. We show that a simple scoring function can discriminate between a large number of competing molecular hypotheses about the upstream cause of the changes observed in a gene expression profile. We then develop an analytical method for computing the statistical significance of each score. This analytical method also helps assess the effects of random or adversarial noise on the predictive power of our model. RESULTS: Our results show that the causal graph we constructed from known biological literature is extremely robust to random noise and to missing or spurious information. We demonstrate the power of our causal reasoning model on two specific examples, one from a cancer dataset and the other from a cardiac hypertrophy experiment. We conclude that causal reasoning models provide a valuable addition to the biologist's toolkit for the interpretation of gene expression data. AVAILABILITY AND IMPLEMENTATION: R source code for the method is available upon request.

Immune Cell Abundance and T-cell Receptor Landscapes Suggest New Patient Stratification Strategies in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is a molecularly and spatially heterogeneous disease frequently characterized by impairment of immunosurveillance mechanisms. Despite recent success with immunotherapy treatment, disease progression still occurs quickly after treatment in the majority of cases, suggesting the need to improve patient selection strategies. In the quest for biomarkers that may help inform response to checkpoint blockade, we characterized the tumor microenvironment (TME) of 162 HNSCC primary tumors of diverse etiologic and spatial origin, through gene expression and IHC profiling of relevant immune proteins, T-cell receptor (TCR) repertoire analysis, and whole-exome sequencing. We identified five HNSCC TME categories based on immune/stromal composition: (i) cytotoxic, (ii) plasma cell rich, (iii) dendritic cell rich, (iv) macrophage rich, and (v) immune-excluded. Remarkably, the cytotoxic and plasma cell rich subgroups exhibited a phenotype similar to tertiary lymphoid structures (TLS), which have been previously linked to immunotherapy response. We also found an increased richness of the TCR repertoire in these two subgroups and in never smokers. Mutational patterns evidencing APOBEC activity were enriched in the plasma cell high subgroup. Furthermore, specific signal propagation patterns within the Ras/ERK and PI3K/AKT pathways associated with distinct immune phenotypes. While traditionally CD8/CD3 T-cell infiltration and immune checkpoint expression (e.g., PD-L1) have been used in the patient selection process for checkpoint blockade treatment, we suggest that additional biomarkers, such as TCR productive clonality, smoking history, and TLS index, may have the ability to pull out potential responders to benefit from immunotherapeutic agents. SIGNIFICANCE: Here we present our findings on the genomic and immune landscape of primary disease in a cohort of 162 patients with HNSCC, benefitting from detailed molecular and clinical characterization. By employing whole-exome sequencing and gene expression analysis of relevant immune markers, TCR profiling, and staining of relevant proteins involved in immune response, we highlight how distinct etiologies, cell intrinsic, and environmental factors combine to shape the landscape of HNSCC primary disease.

Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer.

Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRISPR, followed by manual triage of significantly enriched genes. This triage process to identify 'high value' hits resulting from the CRISPR screen involves manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete. To find key drivers of resistance faster we build a recommendation system on top of a heterogeneous biomedical knowledge graph integrating pre-clinical, clinical, and literature evidence. The recommender system ranks genes based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance. This unbiased approach identifies 57 resistance markers from >3,000 genes, reducing hit identification time from months to minutes. In addition to reproducing known resistance markers, our method identifies previously unexplored resistance mechanisms that we prospectively validate.

Cancer-driving mutations are enriched in genic regions intolerant to germline variation.

Large reference datasets of protein-coding variation in human populations have allowed us to determine which genes and genic subregions are intolerant to germline genetic variation. There is also a growing number of genes implicated in severe Mendelian diseases that overlap with genes implicated in cancer. We hypothesized that cancer-driving mutations might be enriched in genic subregions that are depleted of germline variation relative to somatic variation. We introduce a new metric, OncMTR (oncology missense tolerance ratio), which uses 125,748 exomes in the Genome Aggregation Database (gnomAD) to identify these genic subregions. We demonstrate that OncMTR can significantly predict driver mutations implicated in hematologic malignancies. Divergent OncMTR regions were enriched for cancer-relevant protein domains, and overlaying OncMTR scores on protein structures identified functionally important protein residues. Last, we performed a rare variant, gene-based collapsing analysis on an independent set of 394,694 exomes from the UK Biobank and find that OncMTR markedly improves genetic signals for hematologic malignancies.

Phase Ia/b, Open-Label, Multicenter Study of AZD4635 (an Adenosine A2A Receptor Antagonist) as Monotherapy or Combined with Durvalumab, in Patients with Solid Tumors.

PURPOSE: To evaluate AZD4635, an adenosine A2A receptor antagonist, as monotherapy or in combination with durvalumab in patients with advanced solid tumors. PATIENTS AND METHODS: In phase Ia (dose escalation), patients had relapsed/refractory solid tumors; in phase Ib (dose expansion), patients had checkpoint inhibitor-naïve metastatic castration-resistant prostate cancer (mCRPC) or colorectal carcinoma, non-small cell lung cancer with prior anti-PD-1/PD-L1 exposure, or other solid tumors (checkpoint-naïve or prior anti-PD-1/PD-L1 exposure). Patients received AZD4635 monotherapy (75-200 mg once daily or 125 mg twice daily) or in combination with durvalumab (AZD4635 75 or 100 mg once daily). The primary objective was safety; secondary objectives included antitumor activity and pharmacokinetics; exploratory objectives included evaluation of an adenosine gene signature in patients with mCRPC. RESULTS: As of September 8, 2020, 250 patients were treated (AZD4635, n = 161; AZD4635+durvalumab, n = 89). In phase Ia, DLTs were observed with monotherapy (125 mg twice daily; n = 2) and with combination treatment (75 mg; n = 1) in patients receiving nanosuspension. The most common treatment-related adverse events included nausea, fatigue, vomiting, decreased appetite, dizziness, and diarrhea. The RP2D of the AZD4635 capsule formulation was 75 mg once daily, as monotherapy or in combination with durvalumab. The pharmacokinetic profile was dose-proportional, and exposure was adequate to cover target with 100 mg nanosuspension or 75 mg capsule once daily. In patients with mCRPC receiving monotherapy or combination treatment, tumor responses (2/39 and 6/37, respectively) and prostate-specific antigen responses (3/60 and 10/45, respectively) were observed. High versus low blood-based adenosine signature was associated with median progression-free survival of 21 weeks versus 8.7 weeks. CONCLUSIONS: AZD4635 monotherapy or combination therapy was well tolerated. Objective responses support additional phase II combination studies in patients with mCRPC.

Selection, optimization, and pharmacokinetic properties of a novel, potent antiviral locked nucleic acid-based antisense oligomer targeting hepatitis C virus internal ribosome entry site.

We have screened 47 locked nucleic acid (LNA) antisense oligonucleotides (ASOs) targeting conserved (>95% homology) sequences in the hepatitis C virus (HCV) genome using the subgenomic HCV replicon assay and generated both antiviral (50% effective concentration [EC(50)]) and cytotoxic (50% cytotoxic concentration [CC(50)]) dose-response curves to allow measurement of the selectivity index (SI). This comprehensive approach has identified an LNA ASO with potent antiviral activity (EC(50) = 4 nM) and low cytotoxicity (CC(50) >880 nM) targeting the 25- to 40-nucleotide region (nt) of the HCV internal ribosome entry site (IRES) containing the distal and proximal miR-122 binding sites. LNA ASOs targeting previously known accessible regions of the IRES, namely, loop III and the initiation codon in loop IV, had poor SI values. We optimized the LNA ASO sequence by performing a 1-nucleotide walk through the 25- to 40-nt region and show that the boundaries for antiviral efficacy are extremely precise. Furthermore, we have optimized the format for the LNA ASO using different gapmer and mixomer patterns and show that RNase H is required for antiviral activity. We demonstrate that RNase H-refractory ASOs targeting the 25- to 40-nt region have no antiviral effect, revealing important regulatory features of the 25- to 40-nt region and suggesting that RNase H-refractory LNA ASOs can act as potential surrogates for proviral functions of miR-122. We confirm the antisense mechanism of action using mismatched LNA ASOs. Finally, we have performed pharmacokinetic experiments to demonstrate that the LNA ASOs have a very long half-life (>5 days) and attain hepatic maximum concentrations >100 times the concentration required for in vitro antiviral activity.

Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens.

High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e., off-target effects, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, we interrogate subpopulations carrying sensitivity biomarkers and consecutively investigate unexpectedly resistant (UNRES) cell lines for unique genetic alterations that may drive resistance. By analyzing the GDSC and CTRP datasets, we find 53 and 35 UNRES cases, respectively. For 24 and 28 of them, we highlight putative resistance biomarkers. We find clinically relevant cases such as EGFRT790M mutation in NCI-H1975 or PTEN loss in NCI-H1650 cells, in lung adenocarcinoma treated with EGFR inhibitors. Interrogating the underpinnings of drug resistance with publicly available CRISPR phenotypic assays assists in prioritizing resistance drivers, offering hypotheses for drug combinations.

Adenosine Signaling Is Prognostic for Cancer Outcome and Has Predictive Utility for Immunotherapeutic Response.

PURPOSE: There are several agents in early clinical trials targeting components of the adenosine pathway including A2AR and CD73. The identification of cancers with a significant adenosine drive is critical to understand the potential for these molecules. However, it is challenging to measure tumor adenosine levels at scale, thus novel, clinically tractable biomarkers are needed. EXPERIMENTAL DESIGN: We generated a gene expression signature for the adenosine signaling using regulatory networks derived from the literature and validated this in patients. We applied the signature to large cohorts of disease from The Cancer Genome Atlas (TCGA) and cohorts of immune checkpoint inhibitor-treated patients. RESULTS: The signature captures baseline adenosine levels in vivo (r 2 = 0.92, P = 0.018), is reduced after small-molecule inhibition of A2AR in mice (r 2 = -0.62, P = 0.001) and humans (reduction in 5 of 7 patients, 70%), and is abrogated after A2AR knockout. Analysis of TCGA confirms a negative association between adenosine and overall survival (OS, HR = 0.6, P < 2.2e-16) as well as progression-free survival (PFS, HR = 0.77, P = 0.0000006). Further, adenosine signaling is associated with reduced OS (HR = 0.47, P < 2.2e-16) and PFS (HR = 0.65, P = 0.0000002) in CD8+ T-cell-infiltrated tumors. Mutation of TGFß superfamily members is associated with enhanced adenosine signaling and worse OS (HR = 0.43, P < 2.2e-16). Finally, adenosine signaling is associated with reduced efficacy of anti-PD1 therapy in published cohorts (HR = 0.29, P = 0.00012). CONCLUSIONS: These data support the adenosine pathway as a mediator of a successful antitumor immune response, demonstrate the prognostic potential of the signature for immunotherapy, and inform patient selection strategies for adenosine pathway modulators currently in development.

Screening of highly expressed mycobacterial genes identifies Rv3615c as a useful differential diagnostic antigen for the Mycobacterium tuberculosis complex.

Tuberculous infections caused by mycobacteria, especially tuberculosis of humans and cattle, are important both clinically and economically. Human populations can be vaccinated with Mycobacterium bovis bacille Calmette-Guérin (BCG), and control measures for cattle involving vaccination are now being actively considered. However, diagnostic tests based on tuberculin cannot distinguish between genuine infection and vaccination with BCG. Therefore, identification of differential diagnostic antigens capable of making this distinction is required, and until now sequence-based approaches have been predominant. Here we explored the link between antigenicity and mRNA expression level, as well as the possibility that we may be able to detect differential antigens by analyzing quantified global transcriptional profiles. We generated a list of 14 candidate antigens that are highly expressed in Mycobacterium tuberculosis and M. bovis under a variety of growth conditions. These candidates were screened in M. bovis-infected and naïve cattle for the ability to stimulate a gamma interferon (IFN-gamma) response. We identified one antigen, Rv3615c, which stimulated IFN-gamma responses in a significant proportion of M. bovis-infected cattle (11 of 30 cattle [37%] [P < 0.01]) but not in naïve or BCG-vaccinated animals. Importantly, the same antigen stimulated IFN-gamma responses in a significant proportion of infected cattle that did not respond to the well-characterized mycobacterial antigens ESAT-6 and CFP-10. Therefore, use of the Rv3615c epitope in combination with previously described differential tests based on ESAT-6 and CFP-10 has the potential to significantly increase diagnostic sensitivity without reducing specificity in BCG-vaccinated populations.

Mycobacterium tuberculosis strains disrupted in mce3 and mce4 operons are attenuated in mice.

The Mycobacterium tuberculosis genome contains four copies of an operon called mce (mce1-4). Previously we reported that M. tuberculosis disrupted in the mce1 operon is more virulent than wild-type M. tuberculosis in mice. We generated single deletion mutants in mce3 (Deltamce3) and mce4 (Deltamce4) operons and a double deletion mutant (Deltamce3/4). Similar doubling times and growth characteristics were observed for all mutants and the wild-type (parent) M. tuberculosis H37Rv strain in culture and in macrophages. In addition, similar bacterial burdens were detected in organs from mice infected with Deltamce3 and the parent strain. However, the bacterial burdens of mice infected with Deltamce4 and Deltamce 3/4 were less than those of mice infected with the parent strain. The median survival times of mice infected with wild-type M. tuberculosis, Deltamce3, Deltamce4 and Deltamce3/4 were 40.5, 46, 58 and 62 weeks, respectively. Histopathological examination of lungs at 15 weeks post-infection showed that the extent of the lung lesions was less prominent in mice infected with Deltamce4 and Deltamce 3/4 mutants than in mice infected with the other two strains. These observations suggest that the mce3 and mce4 operons have a role distinct from that of mce1 for in vivo survival of M. tuberculosis.

Network-Based Drug Discovery: Coupling Network Pharmacology with Phenotypic Screening for Neuronal Excitability.

Diseases such as chronic pain with complex etiologies are unlikely to respond to single, target-specific therapeutics but rather require intervention at multiple points within a perturbed disease system. Such approaches are being enabled by the rise of computational methods to identify key points of intervention and by new screening techniques that focus on a relevant condition or phenotype, rather than a specific target. Here we apply an in silico network pharmacology approach to identify small-molecule compounds with the potential to selectively disrupt the structure of a chronic-pain specific disease network, which we validate using a novel phenotypic screen that recapitulates key aspects of neuronal and pain biology by measuring changes in neuronal excitability in native sensory neurons. The combination of network pharmacology with a phenotypic screen is a powerful approach; we show that hit rates increase from 26% to 42%. This represents a rational approach to the discovery of compounds with a poly-pharmacology based therapeutic value, which will be vital for the discovery of treatments for complex disease.

PI3Kα/δ inhibition promotes anti-tumor immunity through direct enhancement of effector CD8+ T-cell activity.

PI3K inhibitors with differential selectivity to distinct PI3K isoforms have been tested extensively in clinical trials, largely to target tumor epithelial cells. PI3K signaling also regulates the immune system and inhibition of PI3Kδ modulate the tumor immune microenvironment of pre-clinical mouse tumor models by relieving T-regs-mediated immunosuppression. PI3K inhibitors as a class and PI3Kδ specifically are associated with immune-related side effects. However, the impact of mixed PI3K inhibitors in tumor immunology is under-explored. Here we examine the differential effects of AZD8835, a dual PI3Kα/δ inhibitor, specifically on the tumor immune microenvironment using syngeneic models. Continuous suppression of PI3Kα/δ was not required for anti-tumor activity, as tumor growth inhibition was potentiated by an intermittent dosing/schedule in vivo. Moreover, PI3Kα/δ inhibition delivered strong single agent anti-tumor activity, which was associated with dynamic suppression of T-regs, improved CD8+ T-cell activation and memory in mouse syngeneic tumor models. Strikingly, AZD8835 promoted robust CD8+ T-cell activation dissociated from its effect on T-regs. This was associated with enhancing effector cell viability/function. Together these data reveal novel mechanisms by which PI3Kα/δ inhibitors interact with the immune system and validate the clinical compound AZD8835 as a novel immunoncology drug, independent of effects on tumor cells. These data support further clinical investigation of PI3K pathway inhibitors as immuno-oncology agents.

Molecular profiling of aged neural progenitors identifies Dbx2 as a candidate regulator of age-associated neurogenic decline.

Adult neurogenesis declines with aging due to the depletion and functional impairment of neural stem/progenitor cells (NSPCs). An improved understanding of the underlying mechanisms that drive age-associated neurogenic deficiency could lead to the development of strategies to alleviate cognitive impairment and facilitate neuroregeneration. An essential step towards this aim is to investigate the molecular changes that occur in NSPC aging on a genomewide scale. In this study, we compare the transcriptional, histone methylation and DNA methylation signatures of NSPCs derived from the subventricular zone (SVZ) of young adult (3 months old) and aged (18 months old) mice. Surprisingly, the transcriptional and epigenomic profiles of SVZ-derived NSPCs are largely unchanged in aged cells. Despite the global similarities, we detect robust age-dependent changes at several hundred genes and regulatory elements, thereby identifying putative regulators of neurogenic decline. Within this list, the homeobox gene Dbx2 is upregulated in vitro and in vivo, and its promoter region has altered histone and DNA methylation levels, in aged NSPCs. Using functional in vitro assays, we show that elevated Dbx2 expression in young adult NSPCs promotes age-related phenotypes, including the reduced proliferation of NSPC cultures and the altered transcript levels of age-associated regulators of NSPC proliferation and differentiation. Depleting Dbx2 in aged NSPCs caused the reverse gene expression changes. Taken together, these results provide new insights into the molecular programmes that are affected during mouse NSPC aging, and uncover a new functional role for Dbx2 in promoting age-related neurogenic decline.

Enhanced mortality despite control of lung infection in mice aerogenically infected with a Mycobacterium tuberculosis mce1 operon mutant.

Mycobacterium tuberculosis causes a variety of host clinical outcomes. We previously showed that M. tuberculosis disrupted in an operon called mce1 proliferates unchecked in BALB/c mouse lungs. The observed outcome could be attributed either to the mutant bacterial burden or to the host immunopathologic response. To differentiate these possibilities, we studied the outcomes of infection in a mouse strain (C57BL/6) less susceptible to M. tuberculosis than BALB/c. We found that the mutant infection reached a plateau in the lungs at a rate similar to that of the wild type. All mice infected with the mutant, but only half of the groups of mice infected with the wild type or complemented strain, died by 40 weeks (p<0.05). At 12-21 weeks of infection, histological examination of the lungs of mice infected with the mutant showed a diffuse pattern of lymphocyte infiltration, while that of mice infected with the other strains exhibited a nodular cellular infiltration pattern. Surprisingly, the number of bacilli recovered from the lungs was similar in all three groups. These observations suggest that rather than the bacterial burden, products of the mce1 operon may directly or indirectly modulate the host immune response that is protective to both the tubercle bacilli and the host.