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Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

In this study we aimed to explore the potential biological effect of ethanol exposure on healthy colon epithelial cells using normal human colon 3D organoid \"mini-gut\" cultures. In numerous published studies ethanol use has been shown to be an environmental risk factor for colorectal cancer (CRC) development; however, the influence of ethanol exposure on normal colon epithelial cell biology remains poorly understood. We investigated the potential molecular effects of ethanol exposure in normal colon 3D organoids in a small pilot study (n = 3) using RNA-seq and ATAC-seq. We identify 1965 differentially expressed genes and 2217 differentially accessible regions of chromatin in response to ethanol treatment. Further, by cross-referencing our results with previously published analysis in colorectal cancer cell lines, we have not only validated a number of reported differentially expressed genes, but also identified several novel candidates for future investigation. In summary, our data highlights the potential importance for the use of normal colon 3D organoid models as a novel tool for the investigation of the relationship between the effects of environmental risk factors associated with colorectal cancer and the molecular mechanisms through which they confer this risk.

Alcohol is a consistently identified risk factor for colon cancer. However, the molecular mechanism underlying its effect on normal colon crypt cells remains poorly understood. We employed RNA-sequencing to asses transcriptomic response to ethanol exposure (0.2% vol:vol) in 3D organoid lines derived from healthy colon (n = 34). Paired regression analysis identified 2,162 differentially expressed genes in response to ethanol. When stratified by colon location, a far greater number of differentially expressed genes were identified in organoids derived from the left versus right colon, many of which corresponded to cell-type specific markers. To test the hypothesis that the effects of ethanol treatment on colon organoid populations were in part due to differential cell composition, we incorporated external single cell RNA-sequencing data from normal colon biopsies to estimate cellular proportions following single cell deconvolution. We inferred cell-type-specific changes, and observed an increase in transit amplifying cells following ethanol exposure that was greater in organoids from the left than right colon, with a concomitant decrease in more differentiated cells. If this occurs in the colon following alcohol consumption, this would lead to an increased zone of cells in the lower crypt where conditions are optimal for cell division and the potential to develop mutations.

Observational studies have shown higher folate consumption to be associated with lower risk of colorectal cancer (CRC). Understanding whether and how genetic risk factors interact with folate could further elucidate the underlying mechanism. Aggregating functionally relevant genetic variants in set-based variant testing has higher power to detect gene–environment (G × E) interactions and may provide information on the underlying biological pathway. We investigated interactions between folate consumption and predicted gene expression on colorectal cancer risk across the genome. We used variant weights from the PrediXcan models of colon tissue-specific gene expression as a priori variant information for a set-based G × E approach. We harmonized total folate intake (mcg/day) based on dietary intake and supplemental use across cohort and case–control studies and calculated sex and study specific quantiles. Analyses were performed using a mixed effects score tests for interactions between folate and genetically predicted expression of 4839 genes with available genetically predicted expression. We pooled results across 23 studies for a total of 13,498 cases with colorectal tumors and 13,918 controls of European ancestry. We used a false discovery rate of 0.2 to identify genes with suggestive evidence of an interaction. We found suggestive evidence of interaction with folate intake on CRC risk for genes including glutathione S-Transferase Alpha 1 ( GSTA1 ; p = 4.3E−4), Tonsuko Like, DNA Repair Protein ( TONSL ; p = 4.3E−4), and Aspartylglucosaminidase ( AGA : p = 4.5E−4). We identified three genes involved in preventing or repairing DNA damage that may interact with folate consumption to alter CRC risk. Glutathione is an antioxidant, preventing cellular damage and is a downstream metabolite of homocysteine and metabolized by GSTA1 . TONSL is part of a complex that functions in the recovery of double strand breaks and AGA plays a role in lysosomal breakdown of glycoprotein.

With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system’s geographical hierarchy.

Sensorineural hearing loss is one of the most common disabilities worldwide. Such prevalence necessitates effective tools for studying the molecular workings of cochlear cells. One prominent and effective vector for expressing genes of interest in research models is adeno-associated virus (AAV). However, AAV efficacy in transducing cochlear cells can vary for a number of reasons including serotype, species, and methodology, and oftentimes requires high multiplicity of infection which can damage the sensory cells. Reports in other systems suggest multiple approaches can be used to enhance AAV transduction including self-complementary vector design and pharmacological inhibition of degradation. Here we produced AAV to drive green fluorescent protein (GFP) expression in explanted neonatal mouse cochleae. Treatment with eeyarestatin I, tyrphostin 23, or lipofectamine 2000 did not result in increased transduction, however, self-complementary vector design resulted in significantly more GFP positive cells when compared to single-stranded controls. Similarly, self-complementary AAV2 vectors demonstrated enhanced transduction efficiency compared to single stranded AAV2 when injected via the posterior semicircular canal, in vivo . Self-complementary vectors for AAV1, 8, and 9 serotypes also demonstrated robust GFP transduction in cochlear cells in vivo , though these were not directly compared to single stranded vectors. These findings suggest that second-strand synthesis may be a rate limiting step in AAV transduction of cochlear tissues and that self-complementary AAV can be used to effectively target large numbers of cochlear cells in vitro and in vivo .

The role of the human gut microbiome in colorectal cancer (CRC) is unclear as most studies on the topic are unable to discern correlation from causation. We apply two-sample Mendelian randomization (MR) to estimate the causal relationship between the gut microbiome and CRC. We used summary-level data from independent genome-wide association studies to estimate the causal effect of 14 microbial traits (n = 3890 individuals) on overall CRC (55,168 cases, 65,160 controls) and site-specific CRC risk, conducting several sensitivity analyses to understand the nature of results. Initial MR analysis suggested that a higher abundance of Bifidobacterium and presence of an unclassified group of bacteria within the Bacteroidales order in the gut increased overall and site-specific CRC risk. However, sensitivity analyses suggested that instruments used to estimate relationships were likely complex and involved in many potential horizontal pleiotropic pathways, demonstrating that caution is needed when interpreting MR analyses with gut microbiome exposures. In assessing reverse causality, we did not find strong evidence that CRC causally affected these microbial traits. Whilst our study initially identified potential causal roles for two microbial traits in CRC, importantly, further exploration of these relationships highlighted that these were unlikely to reflect causality.

Ribonuclease L (RNase L) is an important effector of the innate antiviral response. Mutations or variants that impair function of RNase L, particularly R462Q, have been proposed as susceptibility factors for prostate cancer. Given the role of this gene in viral defense, we sought to explore the possibility that a viral infection might contribute to prostate cancer in individuals harboring the R462Q variant. A viral detection DNA microarray composed of oligonucleotides corresponding to the most conserved sequences of all known viruses identified the presence of gammaretroviral sequences in cDNA samples from seven of 11 R462Q-homozygous (QQ) cases, and in one of eight heterozygous (RQ) and homozygous wild-type (RR) cases. An expanded survey of 86 tumors by specific RT-PCR detected the virus in eight of 20 QQ cases (40%), compared with only one sample (1.5%) among 66 RQ and RR cases. The full-length viral genome was cloned and sequenced independently from three positive QQ cases. The virus, named XMRV, is closely related to xenotropic murine leukemia viruses (MuLVs), but its sequence is clearly distinct from all known members of this group. Comparison of gag and pol sequences from different tumor isolates suggested infection with the same virus in all cases, yet sequence variation was consistent with the infections being independently acquired. Analysis of prostate tissues from XMRV-positive cases by in situ hybridization and immunohistochemistry showed that XMRV nucleic acid and protein can be detected in about 1% of stromal cells, predominantly fibroblasts and hematopoietic elements in regions adjacent to the carcinoma. These data provide to our knowledge the first demonstration that xenotropic MuLV-related viruses can produce an authentic human infection, and strongly implicate RNase L activity in the prevention or clearance of infection in vivo. These findings also raise questions about the possible relationship between exogenous infection and cancer development in genetically susceptible individuals.

Objective Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. A study was conducted in a large multi-population study to assess the feasibility of CRC risk prediction using common genetic variant data combined with other risk factors. A risk prediction model was built and applied to the Scottish population using available data. Design Nine populations of European descent were studied to develop and validate CRC risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence CRC risk. Risk models were generated from case-control data incorporating genotypes alone (n=39 266) and in combination with gender, age and FH (n=11 324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4187 independent samples. The 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks. Results The median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2×10−16), confirmed in external validation sets (Sweden p=1.2×10−6, Finland p=2×10−5). The mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05 to 1.13). Discriminative performance was poor across the risk spectrum (area under curve for genotypes alone 0.57; area under curve for genotype/age/gender/FH 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk. Conclusion Genotype data provide additional information that complements age, gender and FH as risk factors, but individualised genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance.

Introduction Lynch syndrome (LS) is a hereditary condition that increases the risk of colorectal (CRC) and extracolonic cancers that exhibit microsatellite instability (MSI‐H). MSI‐H is driven by defective mismatch repair (dMMR), and approximately 15% of nonhereditary CRCs also exhibit MSI‐H. Here, we aimed to better define mechanisms underlying tumor initiation in LS and MSI‐H cancers through multi‐omic analyses of LS normal colon organoids and MSI‐H tumors. Methods Right (n = 35) and left (n = 23) colon organoids generated from normal colon biopsies at routine colonoscopy of LS and healthy individuals were subjected to Illumina EPIC array. Differentially methylated region (DMR) analysis was performed by DMRcate. RNA‐sequencing (n = 16) and bisulfite‐sequencing (n = 15) were performed on a subset of right colon organoids. CRISPR‐cas9‐mediated editing of MMR genes in colon organoids of healthy individuals was followed by quantitative PCR of MSH4. The relationship between MSH4 expression and tumor mutational burden was further explored in three independent tumor data sets. Results We identified a hypermethylated region of MSH4 in both the right and left colon organoids of LS versus healthy controls, which we validated using bisulfite‐sequencing. DMR analysis in three gastrointestinal and one endometrial data set revealed that this region was also hypermethylated in MSI‐H versus microsatellite stable (MSS) tumors. MSH4 expression was increased in colon organoids of LS versus healthy subjects and in publicly available MSI‐H versus MSS tumors across four RNA‐seq and four microarray data sets. CRISPR‐cas9 editing of MLH1 and MSH2, but not MSH6, in normal colon organoids significantly increased MSH4 expression. MSH4 expression was significantly associated with tumor mutational burden in three publicly available data sets. Conclusions Our findings implicate DNA methylation and gene expression differences of MSH4 as a marker of dMMR and as a potential novel biomarker of LS. Our study of LS colon organoids supports the hypothesis that dMMR exists in the colons of LS subjects prior to CRC.