Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer.

The consensus molecular subtype (CMS) classification of colorectal cancer is based on bulk transcriptomics. The underlying epithelial cell diversity remains unclear. We analyzed 373,058 single-cell transcriptomes from 63 patients, focusing on 49,155 epithelial cells. We identified a pervasive genetic and transcriptomic dichotomy of malignant cells, based on distinct gene expression, DNA copy number and gene regulatory network. We recapitulated these subtypes in bulk transcriptomes from 3,614 patients. The two intrinsic subtypes, iCMS2 and iCMS3, refine CMS. iCMS3 comprises microsatellite unstable (MSI-H) cancers and one-third of microsatellite-stable (MSS) tumors. iCMS3 MSS cancers are transcriptomically more similar to MSI-H cancers than to other MSS cancers. CMS4 cancers had either iCMS2 or iCMS3 epithelium; the latter had the worst prognosis. We defined the intrinsic epithelial axis of colorectal cancer and propose a refined 'IMF' classification with five subtypes, combining intrinsic epithelial subtype (I), microsatellite instability status (M) and fibrosis (F).

Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease.

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.

Natural Reassortants of Potentially Zoonotic Avian Influenza Viruses H5N1 and H9N2 from Egypt Display Distinct Pathogenic Phenotypes in Experimentally Infected Chickens and Ferrets.

The cocirculation of zoonotic highly pathogenic avian influenza virus (HPAIV) of subtype H5N1 and avian influenza virus (AIV) of subtype H9N2 among poultry in Egypt for at least 6 years should render that country a hypothetical hot spot for the emergence of reassortant, phenotypically altered viruses, yet no reassortants have been detected in Egypt. The present investigations proved that reassortants of the Egyptian H5N1 clade 2.2.1.2 virus and H9N2 virus of the G1-B lineage can be generated by coamplification in embryonated chicken eggs. Reassortants were restricted to the H5N1 subtype and acquired between two and all six of the internal segments of the H9N2 virus. Five selected plaque-purified reassortant clones expressed a broad phenotypic spectrum both in vitro and in vivo Two groups of reassortants were characterized to have retarded growth characteristics in vitro compared to the H5N1 parent virus. One clone provoked reduced mortality in inoculated chickens, although the characteristics of a highly pathogenic phenotype were retained. Enhanced zoonotic properties were not predicted for any of these clones, and this prediction was confirmed by ferret inoculation experiments: neither the H5N1 parent virus nor two selected clones induced severe clinical symptoms or were transmitted to sentinel ferrets by contact. While the emergence of reassortants of Egyptian HPAIV of subtype H5N1 with internal gene segments of cocirculating H9N2 viruses is possible in principle, the spread of such viruses is expected to be governed by their fitness to outcompete the parental viruses in the field. The eventual spread of attenuated phenotypes, however, would negatively impact syndrome surveillance on poultry farms and might foster enzootic virus circulation.IMPORTANCE Despite almost 6 years of the continuous cocirculation of highly pathogenic avian influenza virus H5N1 and avian influenza virus H9N2 in poultry in Egypt, no reassortants of the two subtypes have been reported. Here, the principal compatibility of the two subtypes is shown by forcing the reassortment between copassaged H5N1 und H9N2 viruses in embryonated chicken eggs. The resulting reassortant viruses displayed a wide range of pathogenicity including attenuated phenotypes in chickens, but did not show enhanced zoonotic propensities in the ferret model.

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest.

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.

Distinct Host Tropism Protein Signatures to Identify Possible Zoonotic Influenza A Viruses.

Zoonotic influenza A viruses constantly pose a health threat to humans as novel strains occasionally emerge from the avian population to cause human infections. Many past epidemic as well as pandemic strains have originated from avian species. While most viruses are restricted to their primary hosts, zoonotic strains can sometimes arise from mutations or reassortment, leading them to acquire the capability to escape host species barrier and successfully infect a new host. Phylogenetic analyses and genetic markers are useful in tracing the origins of zoonotic infections, but there are still no effective means to identify high risk strains prior to an outbreak. Here we show that distinct host tropism protein signatures can be used to identify possible zoonotic strains in avian species which have the potential to cause human infections. We have discovered that influenza A viruses can now be classified into avian, human, or zoonotic strains based on their host tropism protein signatures. Analysis of all influenza A viruses with complete proteome using the host tropism prediction system, based on machine learning classifications of avian and human viral proteins has uncovered distinct signatures of zoonotic strains as mosaics of avian and human viral proteins. This is in contrast with typical avian or human strains where they show mostly avian or human viral proteins in their signatures respectively. Moreover, we have found that zoonotic strains from the same influenza outbreaks carry similar host tropism protein signatures characteristic of a common ancestry. Our results demonstrate that the distinct host tropism protein signature in zoonotic strains may prove useful in influenza surveillance to rapidly identify potential high risk strains circulating in avian species, which may grant us the foresight in anticipating an impending influenza outbreak.

Predicting host tropism of influenza A virus proteins using random forest.

BACKGROUND: Majority of influenza A viruses reside and circulate among animal populations, seldom infecting humans due to host range restriction. Yet when some avian strains do acquire the ability to overcome species barrier, they might become adapted to humans, replicating efficiently and causing diseases, leading to potential pandemic. With the huge influenza A virus reservoir in wild birds, it is a cause for concern when a new influenza strain emerges with the ability to cross host species barrier, as shown in light of the recent H7N9 outbreak in China. Several influenza proteins have been shown to be major determinants in host tropism. Further understanding and determining host tropism would be important in identifying zoonotic influenza virus strains capable of crossing species barrier and infecting humans. RESULTS: In this study, computational models for 11 influenza proteins have been constructed using the machine learning algorithm random forest for prediction of host tropism. The prediction models were trained on influenza protein sequences isolated from both avian and human samples, which were transformed into amino acid physicochemical properties feature vectors. The results were highly accurate prediction models (ACC>96.57; AUC>0.980; MCC>0.916) capable of determining host tropism of individual influenza proteins. In addition, features from all 11 proteins were used to construct a combined model to predict host tropism of influenza virus strains. This would help assess a novel influenza strain's host range capability. CONCLUSIONS: From the prediction models constructed, all achieved high prediction performance, indicating clear distinctions in both avian and human proteins. When used together as a host tropism prediction system, zoonotic strains could potentially be identified based on different protein prediction results. Understanding and predicting host tropism of influenza proteins lay an important foundation for future work in constructing computation models capable of directly predicting interspecies transmission of influenza viruses. The models are available for prediction at http://fluleap.bic.nus.edu.sg.