Manolis Kellis

Bio: Manolis Kellis is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 128, co-authored 405 publications receiving 112181 citations. Previous affiliations of Manolis Kellis include Broad Institute & Epigenomics AG.

Mathematical analysis of Córdoba calcifediol trial suggests strong role for Vitamin D in reducing ICU admissions of hospitalized COVID-19 patients

Abstract: A randomized controlled trial of calcifediol (25-hydroxyvitamin D3) as a treatment for hospitalized COVID-19 patients in Cordoba, Spain, found that the treatment was associated with reduced ICU admissions with very large effect size and high statistical significance, but the study has had limited impact because it had only 76 patients and imperfect blinding, and did not measure vitamin D levels pre- and post-treatment or adjust for several comorbidities. Here we reanalyze the reported results of the study using rigorous and well established statistical techniques, and find that the randomization, large effect size, and high statistical significance address many of these concerns. We show that random assignment of patients to treatment and control groups is highly unlikely to distribute comorbidities or other prognostic indicators sufficiently unevenly to account for the large effect size. We also show that imperfect blinding would need to have had an implausibly large effect to account for the reported results. Finally, comparison with two additional randomized clinical trials of vitamin D supplementation for COVID-19 in India and Brazil indicates that early intervention and rapid absorption may be crucial for the observed benefits of vitamin D. We conclude that the Cordoba study provides sufficient evidence to warrant immediate, well-designed pivotal clinical trials of early calcifediol administration in a broader cohort of inpatients and outpatients with COVID-19.

Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy

Abstract: The influence of genetic background on driver mutations is well established; however, the mechanisms by which the background interacts with Mendelian loci remains unclear. We performed a systematic secondary-variant burden analysis of two independent Bardet-Biedl syndrome (BBS) cohorts with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants compared to either population controls or to a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes, a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model paradigm for secondary-variant burden analysis in recessive disorders.

Abstract 4282: Deconvolution of plasma-derived exosomes for tracking and prediction of immunotherapy across multiple tissues

Abstract: There is a critical need for robust and minimally invasive biomarkers for predicting and monitoring tumor progression and response to treatment. Transcriptomes of plasma-derived exosomes (PDEs) are suitable candidates to fulfill such a role, since they contain a subtranscriptome of their cell of origin, and, since nearly all cell types secrete exosomes, this allows for the potential monitoring of multiple cell types concurrently. However, a major issue preventing the widespread adoption of plasma-derived exosome as biomarkers is that observed plasma exosomes actually result from a mixture of exosomes from multiple cell types. This confounds detailed dissection and interpretation of putative plasma-derived exosomal biomarkers. To address this issue, we develop a two-component Bayesian deconvolution model to infer the tumor-derived and immune-derived exosomal contribution to the observed mixed plasma-derived exosome signal. Our model leverages transcriptomic information from 3 different sources: (1) paired patient bulk and plasma-derived exosomes, (2) paired cell-line tumor and cell-line tumor-derived exosomes, and (3) healthy control plasma-derived exosomes to learn gene-by-gene mixing fractions between tumor and immune components and the mapping from tumor to tumor-derived exosomes transcriptomic profiles. Using this information, we are able to further infer the patient-specific tumor-derived and immune-derived exosomal transcriptomic profiles for each gene. The outputs from our model enable us to derive tumor-specific and immune-specific exosomal biomarkers. We first show that our model is performant in an extensive set of in silico simulations. Next, we applied our model to transcriptomes collected prior to and during aPD1 immunotherapy treatment from a pilot cohort of N=44 patients (N=29 responders, N=15 nonresponders) with metastatic melanoma. Analysis of our deconvolved profiles yields novel and biologically informative immune-derived and tumor-derived exosomal biomarkers that predict immunotherapy success. Moreover, time-series analysis of the deconvolved profiles show that we are able to identify significantly different tumor and immune related genes whose time dynamics differ significantly between responders and nonresponders, suggesting that plasma-derived exosomes can enable longitudinal tracking of both immune and tumor components of immunotherapy response. Finally, we show that a more sophisticated extension of our deconvolution model is able to provide an estimate of global tumor fraction for each patient, potentially allowing us to infer tumor burden through plasma-derived exosomal transcriptomic signatures. Overall, our plasma-derived exosomal deconvolution model paves the way for more widespread usage of plasma-derived exosomes as a clinical monitoring prediction and monitoring tool. Citation Format: Alvin Shi, Gyulnara Kasumova, Isabel Chien, Jessica Cintolo-Gonzalez, Dennie T. Frederick, Roman Alpatov, William A. Michaud, Deborah Plana, Ryan Corcoran, Keith Flaherty, Ryan Sullivan, Manolis Kellis, Genevieve Boland. Deconvolution of plasma-derived exosomes for tracking and prediction of immunotherapy across multiple tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4282.

Position specific variation in the rate of evolution in transcription factor binding sites

Abstract: BackgroundThe binding sites of sequence specific transcription factors are an important and relatively well-understood class of functional non-coding DNAs. Although a wide variety of experimental and computational methods have been developed to characterize transcription factor binding sites, they remain difficult to identify. Comparison of non-coding DNA from related species has shown considerable promise in identifying these functional non-coding sequences, even though relatively little is known about their evolution.ResultsHere we analyse the genome sequences of the budding yeasts Saccharomyces cerevisiae, S. bayanus, S. paradoxus and S. mikatae to study the evolution of transcription factor binding sites. As expected, we find that both experimentally characterized and computationally predicted binding sites evolve slower than surrounding sequence, consistent with the hypothesis that they are under purifying selection. We also observe position-specific variation in the rate of evolution within binding sites. We find that the position-specific rate of evolution is positively correlated with degeneracy among binding sites within S. cerevisiae. We test theoretical predictions for the rate of evolution at positions where the base frequencies deviate from background due to purifying selection and find reasonable agreement with the observed rates of evolution. Finally, we show how the evolutionary characteristics of real binding motifs can be used to distinguish them from artefacts of computational motif finding algorithms.ConclusionAs has been observed for protein sequences, the rate of evolution in transcription factor binding sites varies with position, suggesting that some regions are under stronger functional constraint than others. This variation likely reflects the varying importance of different positions in the formation of the protein-DNA complex. The characterization of the pattern of evolution in known binding sites will likely contribute to the effective use of comparative sequence data in the identification of transcription factor binding sites and is an important step toward understanding the evolution of functional non-coding DNA.

High depth, whole-genome sequencing of cholera isolates from Haiti and the Dominican Republic

Abstract: Whole-genome sequencing is an important tool for understanding microbial evolution and identifying the emergence of functionally important variants over the course of epidemics. In October 2010, a severe cholera epidemic began in Haiti, with additional cases identified in the neighboring Dominican Republic. We used whole-genome approaches to sequence four Vibrio cholerae isolates from Haiti and the Dominican Republic and three additional V. cholerae isolates to a high depth of coverage (>2000x); four of the seven isolates were previously sequenced. Using these sequence data, we examined the effect of depth of coverage and sequencing platform on genome assembly and identification of sequence variants. We found that 50x coverage is sufficient to construct a whole-genome assembly and to accurately call most variants from 100 base pair paired-end sequencing reads. Phylogenetic analysis between the newly sequenced and thirty-three previously sequenced V. cholerae isolates indicates that the Haitian and Dominican Republic isolates are closest to strains from South Asia. The Haitian and Dominican Republic isolates form a tight cluster, with only four variants unique to individual isolates. These variants are located in the CTX region, the SXT region, and the core genome. Of the 126 mutations identified that separate the Haiti-Dominican Republic cluster from the V. cholerae reference strain (N16961), 73 are non-synonymous changes, and a number of these changes cluster in specific genes and pathways. Sequence variant analyses of V. cholerae isolates, including multiple isolates from the Haitian outbreak, identify coverage-specific and technology-specific effects on variant detection, and provide insight into genomic change and functional evolution during an epidemic.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

STAR: ultrafast universal RNA-seq aligner

Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Full-length transcriptome assembly from RNA-Seq data without a reference genome.

Abstract: Massively parallel sequencing of cDNA has enabled deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here we present the Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available. By efficiently constructing and analyzing sets of de Bruijn graphs, Trinity fully reconstructs a large fraction of transcripts, including alternatively spliced isoforms and transcripts from recently duplicated genes. Compared with other de novo transcriptome assemblers, Trinity recovers more full-length transcripts across a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. Our approach provides a unified solution for transcriptome reconstruction in any sample, especially in the absence of a reference genome.