The SARS-CoV-2 ORF10 is not essential in vitro or in vivo in humans.

SARS-CoV-2 genome annotation revealed the presence of 10 open reading frames (ORFs), of which the last one (ORF10) is positioned downstream of the N gene. It is a hypothetical gene, which was speculated to encode a 38 aa protein. This hypothetical protein does not share sequence similarity with any other known protein and cannot be associated with a function. While the role of this ORF10 was proposed, there is growing evidence showing that the ORF10 is not a coding region. Here, we identified SARS-CoV-2 variants in which the ORF10 gene was prematurely terminated. The disease was not attenuated, and the transmissibility between humans was maintained. Also, in vitro, the strains replicated similarly to the related viruses with the intact ORF10. Altogether, based on clinical observation and laboratory analyses, it appears that the ORF10 protein is not essential in humans. This observation further proves that the ORF10 should not be treated as the protein-coding gene, and the genome annotations should be amended.

Annotating unknown species of urban microorganisms on a global scale unveils novel functional diversity and local environment association.

In urban ecosystems, microbes play a key role in maintaining major ecological functions that directly support human health and city life. However, the knowledge about the species composition and functions involved in urban environments is still limited, which is largely due to the lack of reference genomes in metagenomic studies comprises more than half of unclassified reads. Here we uncovered 732 novel bacterial species from 4728 samples collected from various common surface with the matching materials in the mass transit system across 60 cities by the MetaSUB Consortium. The number of novel species is significantly and positively correlated with the city population, and more novel species can be identified in the skin-associated samples. The in-depth analysis of the new gene catalog showed that the functional terms have a significant geographical distinguishability. Moreover, we revealed that more biosynthetic gene clusters (BGCs) can be found in novel species. The co-occurrence relationship between BGCs and genera and the geographical specificity of BGCs can also provide us more information for the synthesis pathways of natural products. Expanded the known urban microbiome diversity and suggested additional mechanisms for taxonomic and functional characterization of the urban microbiome. Considering the great impact of urban microbiomes on human life, our study can also facilitate the microbial interaction analysis between human and urban environment.

A comprehensive rat transcriptome built from large scale RNA-seq-based annotation.

The rat is an important model organism in biomedical research for studying human disease mechanisms and treatments, but its annotated transcriptome is far from complete. We constructed a Rat Transcriptome Re-annotation named RTR using RNA-seq data from 320 samples in 11 different organs generated by the SEQC consortium. Totally, there are 52 807 genes and 114 152 transcripts in RTR. Transcribed regions and exons in RTR account for ∼42% and ∼6.5% of the genome, respectively. Of all 73 074 newly annotated transcripts in RTR, 34 213 were annotated as high confident coding transcripts and 24 728 as high confident long noncoding transcripts. Different tissues rather than different stages have a significant influence on the expression patterns of transcripts. We also found that 11 715 genes and 15 852 transcripts were expressed in all 11 tissues and that 849 house-keeping genes expressed different isoforms among tissues. This comprehensive transcriptome is freely available at http://www.unimd.org/rtr/. Our new rat transcriptome provides essential reference for genetics and gene expression studies in rat disease and toxicity models.

Replication of Severe Acute Respiratory Syndrome Coronavirus 2 in Human Respiratory Epithelium.

Currently, there are four seasonal coronaviruses associated with relatively mild respiratory tract disease in humans. However, there is also a plethora of animal coronaviruses which have the potential to cross the species border. This regularly results in the emergence of new viruses in humans. In 2002, severe acute respiratory syndrome coronavirus (SARS-CoV) emerged and rapidly disappeared in May 2003. In 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) was identified as a possible threat to humans, but its pandemic potential so far is minimal, as human-to-human transmission is ineffective. The end of 2019 brought us information about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emergence, and the virus rapidly spread in 2020, causing an unprecedented pandemic. At present, studies on the virus are carried out using a surrogate system based on the immortalized simian Vero E6 cell line. This model is convenient for diagnostics, but it has serious limitations and does not allow for understanding of the biology and evolution of the virus. Here, we show that fully differentiated human airway epithelium cultures constitute an excellent model to study infection with the novel human coronavirus SARS-CoV-2. We observed efficient replication of the virus in the tissue, with maximal replication at 2 days postinfection. The virus replicated in ciliated cells and was released apically.IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged by the end of 2019 and rapidly spread in 2020. At present, it is of utmost importance to understand the biology of the virus, rapidly assess the treatment potential of existing drugs, and develop new active compounds. While some animal models for such studies are under development, most of the research is carried out in Vero E6 cells. Here, we propose fully differentiated human airway epithelium cultures as a model for studies on SARS-CoV-2.

Expansion of a SARS-CoV-2 Delta variant with an 872 nt deletion encompassing ORF7a, ORF7b and ORF8, Poland, July to August 2021.

Routine genomic surveillance on samples from COVID-19 patients collected in Poland during summer 2021 revealed the emergence of a SARS-CoV-2 Delta variant with a large 872 nt deletion. This change, confirmed by Sanger and deep sequencing, causes complete loss of ORF7a, ORF7b, and ORF8 genes. The index case carrying the deletion is unknown. The standard pipeline for sequencing may mask this deletion with a long stretch of N's. Effects of this deletion on phenotype or immune evasion needs further study.

Altered cytokine levels and immune responses in patients with SARS-CoV-2 infection and related conditions.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic in early 2020. The infection has been associated with a wide range of clinical symptoms. In the severely affected patients, it has caused dysregulation of immune responses including over-secretion of inflammatory cytokines and imbalances in the proportion of naïve helper T cells, memory helper T cells and regulatory T cells. Identification of the underlying mechanism of such aberrant function of immune system would help in the prediction of disease course and selection of susceptible patients for more intensive cares. In the current review, we summarize the results of studies which reported alterations in cytokine levels and immune cell functions in patients affected with SARS-CoV-2 and related viruses.

Advancements in bioinformatics and computational biology: 15th annual Polish Bioinformatic Society Symposium.

The Polish Bioinformatic Society (PTBI) Symposium convenes annually at leading Polish Universities, and in 2023, the Silesian University of Technology hosted participants from all over the world. The 15th PTBI Symposium, spanning a 3-day duration and divided into four scientific sessions, gathered around 100 participants and centered on research related to machine learning in biomedicine, RNA structure algorithms, next-generation sequencing methods, and microbiome analysis but was not limited to only those topics. The meeting also recognized outstanding research conducted by young scientists by awarding the best poster and best talk. Finally, the awards for the best PhD, MSc, and BSc thesis in bioinformatics defended in Poland were given. This report summarizes the key highlights and outcomes of the meeting.

Microbiome features associated with performance measures in athletic and non-athletic individuals: A case-control study.

The influence of human gut microbiota on health and disease is now commonly appreciated. Therefore, it is not surprising that microbiome research has found interest in the sports community, hoping to improve health and optimize performance. Comparative studies found new species or pathways that were more enriched in elites than sedentary controls. In addition, sport-specific and performance-level-specific microbiome features have been identified. However, the results remain inconclusive and indicate the need for further assessment. In this case-control study, we tested two athletic populations (i.e. strength athletes, endurance athletes) and a non-athletic, but physically active, control group across two acute exercise bouts, separated by a 2-week period, that measured explosive and high intensity fitness level (repeated 30-s all-out Wingate test (WT)) and cardiorespiratory fitness level (Bruce Treadmill Test). While we did not identify any group differences in alpha and beta diversity or significant differential abundance of microbiome components at baseline, one-third of the species identified were unique to each group. Longitudinal sample (pre- and post-exercise) analysis revealed an abundance of Alistipes communis in the strength group during the WT and 88 species with notable between-group differences during the Bruce Test. SparCC recognized Bifidobacterium longum and Bifidobacterium adolescentis, short-chain fatty acid producers with probiotic properties, species strongly associated with VO2max. Ultimately, we identified several taxa with different baseline abundances and longitudinal changes when comparing individuals based on their VO2max, average power, and maximal power parameters. Our results confirmed that the health status of individuals are consistent with assumptions about microbiome health. Furthermore, our findings indicate that microbiome features are associated with better performance previously identified in elite athletes.

Gut microbiome dynamics and predictive value in hospitalized COVID-19 patients: a comparative analysis of shallow and deep shotgun sequencing.

The COVID-19 pandemic caused by SARS-CoV-2 has led to a wide range of clinical presentations, with respiratory symptoms being common. However, emerging evidence suggests that the gastrointestinal (GI) tract is also affected, with angiotensin-converting enzyme 2, a key receptor for SARS-CoV-2, abundantly expressed in the ileum and colon. The virus has been detected in GI tissues and fecal samples, even in cases with negative results of the reverse transcription polymerase chain reaction in the respiratory tract. GI symptoms have been associated with an increased risk of ICU admission and mortality. The gut microbiome, a complex ecosystem of around 40 trillion bacteria, plays a crucial role in immunological and metabolic pathways. Dysbiosis of the gut microbiota, characterized by a loss of beneficial microbes and decreased microbial diversity, has been observed in COVID-19 patients, potentially contributing to disease severity. We conducted a comprehensive gut microbiome study in 204 hospitalized COVID-19 patients using both shallow and deep shotgun sequencing methods. We aimed to track microbiota composition changes induced by hospitalization, link these alterations to clinical procedures (antibiotics administration) and outcomes (ICU referral, survival), and assess the predictive potential of the gut microbiome for COVID-19 prognosis. Shallow shotgun sequencing was evaluated as a cost-effective diagnostic alternative for clinical settings. Our study demonstrated the diverse effects of various combinations of clinical parameters, microbiome profiles, and patient metadata on the precision of outcome prognostication in patients. It indicates that microbiological data possesses greater reliability in forecasting patient outcomes when contrasted with clinical data or metadata. Furthermore, we established that shallow shotgun sequencing presents a viable and cost-effective diagnostic alternative to deep sequencing within clinical environments.

Landscape of global urban environmental resistome and its association with local socioeconomic and medical status.

Antimicrobial resistance (AMR) poses a critical threat to global health and development, with environmental factors-particularly in urban areas-contributing significantly to the spread of antibiotic resistance genes (ARGs). However, most research to date has been conducted at a local level, leaving significant gaps in our understanding of the global status of antibiotic resistance in urban environments. To address this issue, we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples, which were collected by the MetaSUB International Consortium involving diverse urban environments in 60 cities of 27 countries, utilizing a deep-learning based methodology. Our findings demonstrated the strong geographical specificity of urban environmental resistome, and their correlation with various local socioeconomic and medical conditions. We also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters (BGCs) across different countries, and discovered that the urban environment represents a rich source of novel antibiotics. Our study provides a comprehensive overview of the global urban environmental resistome, and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

Mycoparasitism related targets of Tmk1 indicate stimulating regulatory functions of this MAP kinase in Trichoderma atroviride.

Mycoparasitism is a key feature of Trichoderma (Hypocreales, Ascomycota) biocontrol agents. Recent studies of intracellular signal transduction pathways of the potent mycoparasite Trichoderma atroviride revealed the involvement of Tmk1, a mitogen-activated protein kinase (MAPK), in triggering the mycoparasitic response. We previously showed that mutants missing Tmk1 exhibit reduced mycoparasitic activity against several plant pathogenic fungi. In this study, we identified the most robustly regulated targets that were governed by Tmk1 during mycoparasitism using transcriptome and proteome profiling. Tmk1 mainly exerts a stimulating function for T. atroviride during its mycoparasitic interaction with the fungal plant pathogen Rhizoctonia solani, as reflected by 89% of strongly differently responding genes in the ∆tmk1 mutant compared to the wild type. Specifically, 54% of these genes showed strong downregulation in the response with a deletion of the tmk1 gene, whereas in the wild type the same genes were strongly upregulated during the interaction with the fungal host. These included the gene encoding the mycoparasitism-related proteinase Prb1; genes involved in signal transduction pathways such as a candidate coding for a conserved 14-3-3 protein, and a gene coding for Tmk2, the T. atroviride cell-wall integrity MAP kinase; genes encoding a specific siderophore synthetase, and multiple FAD-dependent oxidoreductases and aminotransferases. Due to the phosphorylating activity of Tmk1, different (phospho-)proteomics approaches were applied and identified proteins associated with cellular metabolism, energy production, protein synthesis and fate, and cell organization. Members of FAD- and NAD/NADP-binding-domain proteins, vesicular trafficking of molecules between cellular organelles, fungal translational, as well as protein folding apparatus were among others found to be phosphorylated by Tmk1 during mycoparasitism. Outstanding downregulation in the response of the ∆tmk1 mutant to the fungal host compared to the wild type at both the transcriptome and the proteome levels was observed for nitrilase, indicating that its defense and detoxification functions might be greatly dependent on Tmk1 during T. atroviride mycoparasitism. An intersection network analysis between the identified transcripts and proteins revealed a strong involvement of Tmk1 in molecular functions with GTPase and oxidoreductase activity. These data suggest that during T. atroviride mycoparasitism this MAPK mainly governs processes regulating cell responses to extracellular signals and those involved in reactive oxygen stress.

Iron inactivation by Sporobolomyces ruberrimus and its potential role in plant metal stress protection. An in vitro study.

The endophytic Basidiomycete Sporobolomyces ruberrimus protects its host Arabidopsis arenosa against metal toxicity. Plants inoculated with the fungus yielded more biomass and exhibited significantly fewer stress symptoms in medium mimicking mine dump conditions (medium supplemented with excess of Fe, Zn and Cd). Aside from fine-tuning plant metal homeostasis, the fungus was capable of precipitating Fe in the medium, most likely limiting host exposure to metal toxicity. The precipitated residue was identified by Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), X-Ray Diffraction (XRD) and electron microscopy (SEM/TEM) with energy dispersive X-Ray analysis (EDX/SAED) techniques. The performed analyses revealed that the fungus transforms iron into amorphous (oxy)hydroxides and phosphates and immobilizes them in the form of a precipitate changing Fe behaviour in the MSR medium. Moreover, the complexation of free Fe ions by fungi could be obtained by biomolecules such as lipids, proteins, or biosynthesized redox-active molecules.

Silicone Breast Implant Surface Texture Impacts Gene Expression in Periprosthetic Fibrous Capsules.

BACKGROUND: Silicone breast implants with smooth outer shells are associated with higher rates of capsular contracture, whereas textured implants have been linked to the development of breast implant-associated anaplastic large cell lymphoma. By assessing the gene expression profile of fibrous capsules formed in response to smooth and textured implants, insight into the development of breast implant-associated abnormalities can be gained. METHODS: Miniature smooth or textured silicone implants were surgically inserted into female rats ( n = 10) and harvested for the surrounding capsules at postoperative week 6. RNA sequencing and quantitative polymerase chain reaction were performed to identify genes differentially expressed between smooth and textured capsules. For clinical correlation, the expression of candidate genes was assayed in implant capsules harvested from human patients with and without capsular contracture. RESULTS: Of 18,555 differentially expressed transcripts identified, three candidate genes were selected: matrix metalloproteinase-3 ( MMP3 ), troponin-T3 ( TNNT3 ), and neuregulin-1 ( NRG1 ). In textured capsules, relative gene expression and immunostaining of MMP3 and TNNT3 was up-regulated, whereas NRG1 was down-regulated compared to smooth capsules [mean relative fold change, 8.79 ( P = 0.0059), 4.81 ( P = 0.0056), and 0.40 ( P < 0.0001), respectively]. Immunostaining of human specimens with capsular contracture revealed similar gene expression patterns to those of animal-derived smooth capsules. CONCLUSIONS: An expression pattern of low MMP3 /low TNNT3 /high NRG1 is specifically associated with smooth implant capsules and human implant capsules with capsular contracture. The authors' clinically relevant breast implant rat model provides a strong foundation to further explore the molecular genetics of implant texture and its effect on breast implant-associated abnormalities. CLINICAL RELEVANCE STATEMENT: The authors have demonstrated that there are distinct gene expression profiles in response to smooth versus textured breast implants. Since surface texture may be linked to implant-related pathology, further molecular analysis of periprosthetic capsules may yield strategies to mitigate implant-related complications.

SARS-CoV-2 infection impairs NK cell functions via activation of the LLT1-CD161 axis.

Introduction: Natural killer (NK) cells plays a pivotal role in the control of viral infections, and their function depend on the balance between their activating and inhibitory receptors. The immune dysregulation observed in COVID-19 patients was previously associated with downregulation of NK cell numbers and function, yet the mechanism of inhibition of NK cell functions and the interplay between infected cells and NK cells remain largely unknown. Methods: In this study we show that SARS-CoV-2 infection of airway epithelial cells can directly influence NK cell phenotype and functions in the infection microenvironment. NK cells were co-cultured with SARS-CoV-2 infected epithelial cells, in a direct contact with A549ACE2/TMPRSS2 cell line or in a microenvironment of the infection in a 3D ex vivo human airway epithelium (HAE) model and NK cell surface expression of a set of most important receptors (CD16, NKG2D, NKp46, DNAM-1, NKG2C, CD161, NKG2A, TIM-3, TIGIT, and PD-1) was analyzed. Results: We observed a selective, in both utilized experimental models, significant downregulation the proportion of CD161 (NKR-P1A or KLRB1) expressing NK cells, and its expression level, which was followed by a significant impairment of NK cells cytotoxicity level against K562 cells. What is more, we confirmed that SARS-CoV-2 infection upregulates the expression of the ligand for CD161 receptor, lectin-like transcript 1 (LLT1, CLEC2D or OCIL), on infected epithelial cells. LLT1 protein can be also detected not only in supernatants of SARS-CoV-2 infected A549ACE2/TMPRSS2 cells and HAE basolateral medium, but also in serum of COVID-19 patients. Finally, we proved that soluble LLT1 protein treatment of NK cells significantly reduces i) the proportion of CD161+ NK cells, ii) the ability of NK cells to control SARS-CoV-2 infection in A549ACE2/TMPRSS2 cells and iii) the production of granzyme B by NK cells and their cytotoxicity capacity, yet not degranulation level. Conclusion: We propose a novel mechanism of SARS-CoV-2 inhibition of NK cell functions via activation of the LLT1-CD161 axis.

RNA-seq data science: From raw data to effective interpretation.

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling.

MOTIVATION: Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not. With the compilation of large-scale RNA-Seq datasets with technical replicate samples, however, we can now, for the first time, perform a systematic analysis of the precision of expression level estimates from massively parallel sequencing technology. This then allows considerations for its improvement by computational or experimental means. RESULTS: We report on a comprehensive study of target identification and measurement precision, including their dependence on transcript expression levels, read depth and other parameters. In particular, an impressive recall of 84% of the estimated true transcript population could be achieved with 331 million 50 bp reads, with diminishing returns from longer read lengths and even less gains from increased sequencing depths. Most of the measurement power (75%) is spent on only 7% of the known transcriptome, however, making less strongly expressed transcripts harder to measure. Consequently, <30% of all transcripts could be quantified reliably with a relative error<20%. Based on established tools, we then introduce a new approach for mapping and analysing sequencing reads that yields substantially improved performance in gene expression profiling, increasing the number of transcripts that can reliably be quantified to over 40%. Extrapolations to higher sequencing depths highlight the need for efficient complementary steps. In discussion we outline possible experimental and computational strategies for further improvements in quantification precision. CONTACT: rnaseq10@boku.ac.at

Scientists without borders: lessons from Ukraine.

Conflicts and natural disasters affect entire populations of the countries involved and, in addition to the thousands of lives destroyed, have a substantial negative impact on the scientific advances these countries provide. The unprovoked invasion of Ukraine by Russia, the devastating earthquake in Turkey and Syria, and the ongoing conflicts in the Middle East are just a few examples. Millions of people have been killed or displaced, their futures uncertain. These events have resulted in extensive infrastructure collapse, with loss of electricity, transportation, and access to services. Schools, universities, and research centers have been destroyed along with decades' worth of data, samples, and findings. Scholars in disaster areas face short- and long-term problems in terms of what they can accomplish now for obtaining grants and for employment in the long run. In our interconnected world, conflicts and disasters are no longer a local problem but have wide-ranging impacts on the entire world, both now and in the future. Here, we focus on the current and ongoing impact of war on the scientific community within Ukraine and from this draw lessons that can be applied to all affected countries where scientists at risk are facing hardship. We present and classify examples of effective and feasible mechanisms used to support researchers in countries facing hardship and discuss how these can be implemented with help from the international scientific community and what more is desperately needed. Reaching out, providing accessible training opportunities, and developing collaborations should increase inclusion and connectivity, support scientific advancements within affected communities, and expedite postwar and disaster recovery.

A history of the MetaSUB consortium: Tracking urban microbes around the globe.

The MetaSUB Consortium, founded in 2015, is a global consortium with an interdisciplinary team of clinicians, scientists, bioinformaticians, engineers, and designers, with members from more than 100 countries across the globe. This network has continually collected samples from urban and rural sites including subways and transit systems, sewage systems, hospitals, and other environmental sampling. These collections have been ongoing since 2015 and have continued when possible, even throughout the COVID-19 pandemic. The consortium has optimized their workflow for the collection, isolation, and sequencing of DNA and RNA collected from these various sites and processing them for metagenomics analysis, including the identification of SARS-CoV-2 and its variants. Here, the Consortium describes its foundations, and its ongoing work to expand on this network and to focus its scope on the mapping, annotation, and prediction of emerging pathogens, mapping microbial evolution and antibiotic resistance, and the discovery of novel organisms and biosynthetic gene clusters.

An analysis of single amino acid repeats as use case for application specific background models.

BACKGROUND: Sequence analysis aims to identify biologically relevant signals against a backdrop of functionally meaningless variation. Increasingly, it is recognized that the quality of the background model directly affects the performance of analyses. State-of-the-art approaches rely on classical sequence models that are adapted to the studied dataset. Although performing well in the analysis of globular protein domains, these models break down in regions of stronger compositional bias or low complexity. While these regions are typically filtered, there is increasing anecdotal evidence of functional roles. This motivates an exploration of more complex sequence models and application-specific approaches for the investigation of biased regions. RESULTS: Traditional Markov-chains and application-specific regression models are compared using the example of predicting runs of single amino acids, a particularly simple class of biased regions. Cross-fold validation experiments reveal that the alternative regression models capture the multi-variate trends well, despite their low dimensionality and in contrast even to higher-order Markov-predictors. We show how the significance of unusual observations can be computed for such empirical models. The power of a dedicated model in the detection of biologically interesting signals is then demonstrated in an analysis identifying the unexpected enrichment of contiguous leucine-repeats in signal-peptides. Considering different reference sets, we show how the question examined actually defines what constitutes the 'background'. Results can thus be highly sensitive to the choice of appropriate model training sets. Conversely, the choice of reference data determines the questions that can be investigated in an analysis. CONCLUSIONS: Using a specific case of studying biased regions as an example, we have demonstrated that the construction of application-specific background models is both necessary and feasible in a challenging sequence analysis situation.

Tumor infiltrating lymphocyte signature is associated with single nucleotide polymorphisms and predicts survival in esophageal squamous cell carcinoma patients.

PURPOSE: Esophageal cancer is the sixth leading cause of cancer-related death worldwide, and is associated with a poor prognosis. Stromal tumor infiltrating lymphocytes (sTIL) and certain single nucleotide polymorphisms (SNPs) have been found to be predictive of patient survival. In this study, we explored the association between SNPs and sTIL regarding the predictability of disease-free survival in patients with esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS: We collected 969 pathologically confirmed ESCC patients from 2010 to 2013 and genotyped 101 SNPs from 59 genes. The number of sTIL for each patient was determined using an automatic algorithm. A Kruskal-Wallis test was used to determine the association between genotype and sTIL. The genotypes and clinical factors related to survival were analyzed using a Kaplan-Meier curve, Cox proportional hazards model, and log-rank test. RESULTS: The median age of the patients was 67 (42-85 years), there was a median follow-up of 851.5 days and 586 patients died. The univariable analysis showed that 10 of the 101 SNPs were associated with sTIL. Six SNPs were also associated with disease-free survival. A multivariable analysis revealed that sTIL, rs1801131, rs25487, and rs8030672 were independent prognostic markers for ESCC patients. The model combining SNPs, clinical characteristics and sTIL outperformed the model with clinical characteristics alone for predicting outcomes in ESCC patients. CONCLUSION: We discovered 10 SNPs associated with sTIL in ESCC and we built a model of sTIL, SNPs and clinical characteristics with improved prediction of survival in ESCC patients.