Biology subjects

Salama, S.

Publications and source records attributed to Salama, S..

15 recordsLinked to original sources

bioRxiv2026

Evaluating genome assemblies with HMM-Flagger

HMM-Flagger is a reference-free tool for detecting structural errors in haplotype-resolved genome assemblies based upon the coverage of mapped reads. It models read coverage with a hidden Markov model augmented by a Gaussian autoregressive process, which enables classifying coverage anomalies as erroneous blocks, false duplications, or collapsed blocks. Trained and tested on synthetic misassemblies, it detected synthetic errors using Pacific Biosciences HiFi and Oxford Nanopore Technologies R10 data with F1 scores of 78.4% and 60.4% respectively. When applied to six HG002 assemblies it revealed multiple large misassemblies including false duplications and collapse events in human satellites. Applied to assemblies from the Human Pangenome Reference Consortium (HPRC), HMM-Flagger demonstrated substantial improvements from release 1 (0.94% error rate) to release 2 (0.38%), reflecting technological advances. HMM-Flagger also validated NOTCH2NL assemblies in HPRC release 2 and confirmed the correctness of three novel structural configurations.

bioinformatics↗

bioRxiv2025

PERM1 Gene Delivery via AAV Prevents Heart Failure in a Mouse Model of Pressure Overload

Heart failure with reduced ejection fraction (HFrEF) remains a leading cause of mortality worldwide. A hallmark of HFrEF is impaired cardiomyocyte contractility accompanied by disrupted mitochondrial bioenergetics; however, no current therapy targets both pathologies simultaneously. PERM1, a striated muscle-specific regulator of mitochondrial bioenergetics, is downregulated in HFrEF patients. We recently demonstrated that overexpression of PERM1 via adeno-associated virus 9 (AAV9-PERM1) enhances both cardiac contractility and mitochondrial biogenesis in C57BL/6 mice. In this study, we evaluated the therapeutic potential of AAV9-PERM1 in a pressure overload-induced mouse model of HFrEF. C57BL/6 mice were treated with either AAV9-PERM1 or control AAV9-GFP (1x1012 GC/mouse), followed by transverse aortic constriction (TAC) surgery. At 4 weeks post-TAC, control mice receiving AAV-GFP exhibited reduced left ventricular ejection fraction (LVEF), whereas AAV-PERM1 preserved LVEF at baseline levels. This cardioprotective effect was sustained through 8 weeks. Notably, AAV9-PERM1 completely abrogated TAC-induced cardiac hypertrophy and fibrosis. Mitochondrial analysis revealed that AAV9-PERM1 preserved mitochondrial DNA copy number and TFAM protein levels, both of which were reduced by TAC in control hearts. AAV9-PERM1 also improved mitochondrial respiration using pyruvate and octanoylcarnitine as substrates and prevented TAC-induced impairments in oxidative capacity. While PGC-1 expression remained unchanged in control TAC hearts, it was modestly yet significantly upregulated by AAV9-PERM1 in both sham and TAC groups. In addition, AAV9-PERM1 suppressed TAC-induced increases in O-GlcNAcylation, a stress-related posttranslational modification of proteins. Co-immunoprecipitation further revealed interactions of PERM1 with creatine kinase and troponin C, key proteins in ATP transduction and contractility, suggesting a functional coupling between mitochondrial energetics and contractility. In conclusion, AAV-PERM1 gene therapy effectively preserves cardiac function under pressure overload by maintaining mitochondrial biogenesis, respiration capacity and contractility. This study further suggests AAV-PERM1 as a promising therapeutic strategy for HFrEF.

physiology↗

bioRxiv2025

Evidence of Escherichia coli Regulating Drosophila melanogaster Behavior

Chemosensory systems are critical for insect survival, enabling host seeking, food acquisition, and oviposition site selection. While insect-associated microbes are known to influence host development and immunity, their role in modulating chemosensory behavior remains poorly understood. Here, we demonstrate that Escherichia coli, a bacterium identified in non-gut tissues of Aedes albopictus and experimentally reintroduced into axenic Drosophila melanogaster, alters both larval and adult sensory-driven behaviors. In larvae, E. coli infection modified phototaxis and mechanosensory responses across genotypes, while tunneling and thermosensory behaviors were specifically dependent on the ionotropic receptor co-receptor IR25a. In adults, E. coli increased attraction to fermentation cues (apple cider vinegar, ethanol) and enhanced sucrose consumption in wild-type and Orco-deficient flies, but not in IR25a-deficient mutants. Gas chromatography-mass spectrometry revealed that E. coli shifted cuticular hydrocarbon composition toward shorter-chain alkanes and increased the sex pheromone 9-tricosene in an IR25a-dependent manner. Together, these findings show that E. coli broadly reprograms insect sensory behavior, with IR25a serving as a critical mediator of microbial influence on chemosensory and physiological traits. This work identifies a previously unrecognized role for E. coli in shaping insect behavior and chemical ecology, providing a foundation for investigating microbial contributions to host-microbe coevolution and for exploring microbial cues as novel, environmentally sustainable tools for insect control. ImportanceInsects depend on their sense of smell and taste to find food, mates, and safe places to reproduce. These behaviors drive the spread of insect-borne diseases and cause major agricultural losses worldwide. Our work shows that a common bacterium, Escherichia coli, can reshape insect sensory behaviors, influencing how flies respond to light, temperature, food, and even the chemical signals on their outer surface that control mating. Importantly, these effects are linked to a specific sensory receptor, IR25a, which is conserved across many insect species. This finding reveals a previously unknown role for microbes in fine-tuning insect behavior. Understanding how bacteria influence insect sensory systems not only provides insight into host-microbe interactions but also opens the door to new, environmentally friendly ways of managing insect pests by targeting their behavior rather than killing them outright.

microbiology↗

bioRxiv2025

Genetic diversity and regulatory features of human-specific NOTCH2NL duplications

NOTCH2NL (NOTCH2-N-terminus-like) genes arose from ape-specific chromosome 1 segmental duplications implicated in human brain cortical expansion, including an incomplete NOTCH2 gene. Genetic characterization of these loci and their regulation is complicated because they are embedded in large, nearly identical duplications that predispose to recurrent microdeletion syndromes. Using near-complete long-read assemblies generated from 70 human and 12 ape haploid genomes, we show independent recurrent duplication among apes with protein-coding copies emerging in humans 2.2-3.7 million years ago. We distinguish NOTCH2NL paralogs present in every human haplotype (NOTCH2NLA) from copy number variable ones. We also characterize large-scale structural variation, including gene conversion, for 28% of haplotypes leading to a previously undescribed paralog, NOTCH2tv. Finally, we apply Fiber-seq and long-read transcript sequencing to human dorsal forebrain organoids to characterize the regulatory landscape and find that the most fixed paralogs, NOTCH2 and NOTCH2NLA, harbor the greatest number of paralog-specific elements potentially driving their regulation.

genomics↗

bioRxiv2025

HIPPIE: A Multimodal Deep Learning Model for Electrophysiological Classification of Neurons

Extracellular electrophysiological recordings present unique computational challenges for neuronal classification due to noise, technical variability, and batch effects across experimental systems. We introduce HIPPIE (High-dimensional Interpretation of Physiological Patterns In Extracellular recordings), a deep learning framework that combines self-supervised pretraining on unlabeled datasets with supervised fine-tuning to classify neurons from extracellular recordings. Using conditional convolutional joint autoencoders, HIPPIE learns robust, technology-adjusted representations of waveforms and spiking dynamics. This model can be applied to electrophysiological classification and clustering across diverse biological cultures and technologies. We validated HIPPIE on both in vivo mouse recordings and in vitro brain slices, where it demonstrated superior performance over other unsupervised methods in cell-type discrimination and aligned closely with anatomically defined classes. Its latent space organizes neurons along electrophysiological gradients, while enabling batch and individual corrected alignment of recordings across experiments. HIPPIE establishes a general framework for systematically decoding neuronal diversity in native and engineered systems.

neuroscience↗

bioRxiv2023

Isoform-specific translational control is evolutionarily conserved in primates

Alternative splicing (AS) alters messenger RNA (mRNA) coding capacity, localization, stability, and translation. Here we use comparative transcriptomics to identify cis-acting elements coupling AS to translational control (AS-TC). We sequenced total cytosolic and polyribosome-associated mRNA from human, chimpanzee, and orangutan induced pluripotent stem cells (iPSCs), revealing thousands of transcripts with splicing differences between subcellular fractions. We found both conserved and species-specific polyribosome association patterns for orthologous splicing events. Intriguingly, alternative exons with similar polyribosome profiles between species have stronger sequence conservation than exons with lineage-specific ribosome association. These data suggest that sequence variation underlies differences in the polyribosome association. Accordingly, single nucleotide substitutions in luciferase reporters designed to model exons with divergent polyribosome profiles are sufficient to regulate translational efficiency. We used position specific weight matrixes to interpret exons with species-specific polyribosome association profiles, finding that polymorphic sites frequently alter recognition motifs for trans-acting RNA binding proteins. Together, our results show that AS can regulate translation by remodeling the cis-regulatory landscape of mRNA isoforms.

molecular biology↗

bioRxiv2021

Complete genomic and epigenetic maps of human centromeres

Existing human genome assemblies have almost entirely excluded highly repetitive sequences within and near centromeres, limiting our understanding of their sequence, evolution, and essential role in chromosome segregation. Here, we present an extensive study of newly assembled peri/centromeric sequences representing 6.2% (189.9 Mb) of the first complete, telomere-to-telomere human genome assembly (T2T-CHM13). We discovered novel patterns of peri/centromeric repeat organization, variation, and evolution at both large and small length scales. We also found that inner kinetochore proteins tend to overlap the most recently duplicated subregions within centromeres. Finally, we compared chromosome X centromeres across a diverse panel of individuals and uncovered structural, epigenetic, and sequence variation at single-base resolution across these regions. In total, this work provides an unprecedented atlas of human centromeres to guide future studies of their complex and critical functions as well as their unique evolutionary dynamics. One-sentence summaryDeep characterization of fully assembled human centromeres reveals their architecture and fine-scale organization, variation, and evolution.

genomics↗

bioRxiv2021

Development of a Low-Cost System for Simultaneous Longitudinal Biological Imaging

Simultaneous longitudinal imaging across multiple conditions and replicates has been crucial for scientific studies aiming to understand biological processes and disease. Yet, imaging systems capable of accomplishing these tasks are economically unattainable for most academic and teaching laboratories around the world. Here we propose the Picroscope, which is the first low cost system for simultaneous longitudinal biological imaging made primarily using off-the-shelf and 3D-printed materials. The Picroscope is compatible with standard 24-well cell culture plates and captures 3D z-stack image data. The Picroscope can be controlled remotely, allowing for automatic imaging with minimal intervention from the investigator. Here we use this system in a range of applications. We gathered longitudinal whole organism image data for frogs, zebrafish and planaria worms. We also gathered image data inside an incubator to observe 2D monolayers and 3D mammalian tissue culture models. Using this tool, we can measure the behavior of entire organisms or individual cells over long time periods.

bioengineering↗

bioRxiv2019

Framework for determining accuracy of RNA sequencing data for gene expression profiling of single samples

BackgroundThe accuracy of gene expression as measured by RNA sequencing (RNA-Seq) is dependent on the amount of sequencing performed. However, some types of reads are not informative for determining this accuracy. Unmapped and non-exonic reads do not contribute to gene expression quantification. Duplicate reads can be the product of high gene expression or technical errors. FindingsWe surveyed bulk RNA-Seq datasets from 2179 tumors in 48 cohorts to determine the fractions of uninformative reads. Total sequence depth was 0.2-668 million reads (median (med.) 61 million; interquartile range (IQR) 53 million). Unmapped reads constitute 1-77% of all reads (med. 3%; IQR 3%); duplicate reads constitute 3-100% of mapped reads (med. 27%; IQR 30%); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (med. 25%; IQR 21%). Informative reads--Mapped, Exonic, Non-duplicate (MEND) reads--constitute 0-79% of total reads (med. 50%; IQR 31%). Further, we find that MEND read counts have a 0.22 Pearson correlation to the number of genes expressed above 1 Transcript Per Million, while total reads have a correlation of -0.05. ConclusionsSince the fraction of uninformative reads vary, we propose using only definitively informative reads, MEND reads, for the purposes of asserting the accuracy of gene expression measured in a bulk RNA-Seq experiment. We provide a Docker image containing 1) the existing required tools (RSeQC, sambamba and samblaster) and 2) a custom script. We recommend that all results, sensitivity studies and depth recommendations use MEND units.

genomics↗

bioRxiv2019

Efficient de novo assembly of eleven human genomes using PromethION sequencing and a novel nanopore toolkit

Present workflows for producing human genome assemblies from long-read technologies have cost and production time bottlenecks that prohibit efficient scaling to large cohorts. We demonstrate an optimized PromethION nanopore sequencing method for eleven human genomes. The sequencing, performed on one machine in nine days, achieved an average 63x coverage, 42 Kb read N50, 90% median read identity and 6.5x coverage in 100 Kb+ reads using just three flow cells per sample. To assemble these data we introduce new computational tools: Shasta - a de novo long read assembler, and MarginPolish & HELEN - a suite of nanopore assembly polishing algorithms. On a single commercial compute node Shasta can produce a complete human genome assembly in under six hours, and MarginPolish & HELEN can polish the result in just over a day, achieving 99.9% identity (QV30) for haploid samples from nanopore reads alone. We evaluate assembly performance for diploid, haploid and trio-binned human samples in terms of accuracy, cost, and time and demonstrate improvements relative to current state-of-the-art methods in all areas. We further show that addition of proximity ligation (Hi-C) sequencing yields near chromosome-level scaffolds for all eleven genomes.

bioinformatics↗

bioRxiv2019

ProTECT: Prediction of T-cell Epitopes for Cancer Therapy

Somatic mutations in cancers affecting protein coding genes can give rise to potentially therapeutic neoepitopes. These neoepitopes can guide Adoptive Cell Therapies (ACTs) and Peptide Vaccines (PVs) to selectively target tumor cells using autologous patient cytotoxic T-cells. Currently, researchers have to independently align their data, call somatic mutations and haplotype the patients HLA to use existing neoepitope prediction tools. We present ProTECT, a fully automated, reproducible, scalable, and efficient end-to-end analysis pipeline to identify and rank therapeutically relevant tumor neoepitopes in terms of immunogenicity starting directly from raw patient sequencing data, or from pre-processed data. The ProTECT pipeline encompasses alignment, HLA haplotyping, mutation calling (single nucleotide variants, short insertions and deletions, and gene fusions), peptide:MHC (pMHC) binding prediction, and ranking of final candidates. We demonstrate ProTECT on 326 samples from the TCGA Prostate Adenocarcinoma cohort, and compare it with published tools. ProTECT can be run on a standalone computer, a local cluster, or on a compute cloud using a Mesos backend. ProTECT is highly scalable and can process TCGA data in under 30 minutes per sample when run in large batches. ProTECT is freely available at https://www.github.com/BD2KGenomics/protect.

bioinformatics↗

bioRxiv2019

Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment

AimsThe residual cancer burden index is an important quantitative measure used for assessing treatment response following neoadjuvant therapy for breast cancer. It has shown to be predictive of overall survival and is composed of two key metrics: qualitative assessment of lymph nodes and the percentage of invasive or in-situ tumour cellularity (TC) in the tumour bed (TB). Currently, TC is assessed through eye-balling of routine histopathology slides estimating the proportion of tumour cells within the TB. With the advances in production of digitized slides and increasing availability of slide scanners in pathology laboratories, there is potential to measure TC using automated algorithms with greater precision and accuracy.\n\nMethodsWe describe two methods for automated TC scoring: 1) a traditional approach to image analysis development whereby we mimic the pathologists workflow, and 2) a recent development in artificial intelligence in which features are learned automatically in deep neural networks using image data alone.\n\nResultsWe show strong agreements between automated and manual analysis of digital slides. Agreements between our trained deep neural networks and experts in this study (0.82) approach the inter-rater agreements between pathologists (0.89). We also reveal properties that are captured when we apply deep neural network to whole slide images, and discuss the potential of using such visualisations to improve upon TC assessment in the future.\n\nConclusionsTC scoring can be successfully automated by leveraging recent advancements in artificial intelligence, thereby alleviating the burden of manual analysis.

pathology↗

bioRxiv2018

The UCSC Repeat Browser allows discovery and visualization of evolutionary conflict across repeat families

BackgroundNearly half the human genome consists of repeat elements, most of which are retrotransposons, and many of these sequences play important biological roles. However repeat elements pose several unique challenges to current bioinformatic analyses and visualization tools, as short repeat sequences can map to multiple genomic loci resulting in their misclassification and misinterpretation. In fact, sequence data mapping to repeat elements are often discarded from analysis pipelines. Therefore, there is a continued need for standardized tools and techniques to interpret genomic data of repeats. ResultsWe present the UCSC Repeat Browser, which consists of a complete set of human repeat reference sequences derived from the gold standard repeat database RepeatMasker. The UCSC Repeat Browser contains mapped annotations from the human genome to these references, and presents all of them as a comprehensive interface to facilitate work with repetitive elements. Furthermore, it provides processed tracks of multiple publicly available datasets of biological interest to the repeat community, including ChIP-SEQ datasets for KRAB Zinc Finger Proteins (KZNFs) - a family of proteins known to bind and repress certain classes of repeats. Here we show how the UCSC Repeat Browser in combination with these datasets, as well as RepeatMasker annotations in several non-human primates, can be used to trace the independent trajectories of species-specific evolutionary conflicts. ConclusionsThe UCSC Repeat Browser allows easy and intuitive visualization of genomic data on consensus repeat elements, circumventing the problem of multi-mapping, in which sequencing reads of repeat elements map to multiple locations on the human genome. By developing a reference consensus, multiple datasets and annotation tracks can easily be overlaid to reveal complex evolutionary histories of repeats in a single interactive window. Specifically, we use this approach to retrace the history of several primate specific LINE-1 families across apes, and discover several species-specific routes of evolution that correlate with the emergence and binding of KZNFs.

genomics↗

bioRxiv2018

KRAB Zinc Finger Proteins coordinate across evolutionary time scales to battle retroelements

KRAB Zinc Finger Proteins (KZNFs) are the largest and fastest evolving family of human transcription factors1,2. The evolution of this protein family is closely linked to the tempo of retrotransposable element (RTE) invasions, with specific KZNF family members demonstrated to transcriptionally repress specific families of RTEs3,4. The competing selective pressures between RTEs and the KZNFs results in evolutionary arms races whereby KZNFs evolve to recognize RTEs, while RTEs evolve to escape KZNF recognition5. Evolutionary analyses of the primate-specific RTE family L1PA and two of its KZNF binders, ZNF93 and ZNF649, reveal specific nucleotide and amino changes consistent with an arms race scenario. Our results suggest a model whereby ZNF649 and ZNF93 worked together to target independent motifs within the L1PA RTE lineage. L1PA elements eventually escaped the concerted action of this KZNF \"team\" over [~]30 million years through two distinct mechanisms: a slow accumulation of point mutations in the ZNF649 binding site and a rapid, massive deletion of the entire ZNF93 binding site.

evolutionary biology↗

bioRxiv2016

Cis-Compound Mutations are Prevalent in Triple Negative Breast Cancer and Can Drive Tumor Progression

About 16% of breast cancers fall into a clinically aggressive category designated triple negative (TNBC) due to a lack of ERBB2, estrogen receptor and progesterone receptor expression1-3. The mutational spectrum of TNBC has been characterized as part of The Cancer Genome Atlas (TCGA)4; however, snapshots of primary tumors cannot reveal the mechanisms by which TNBCs progress and spread. To address this limitation we initiated the Intensive Trial of OMics in Cancer (ITOMIC)-001, in which patients with metastatic TNBC undergo multiple biopsies over space and time5. Whole exome sequencing (WES) of 67 samples from 11 patients identified 426 genes containing multiple distinct single nucleotide variants (SNVs) within the same sample, instances we term Multiple SNVs affecting the Same Gene and Sample (MSSGS). We find that >90% of MSSGS result from cis-compound mutations (in which both SNVs affect the same allele), that MSSGS comprised of SNVs affecting adjacent nucleotides arise from single mutational events, and that most other MSSGS result from the sequential acquisition of SNVs. Some MSSGS drive cancer progression, as exemplified by a TNBC driven by FGFR2(S252W;Y375C). MSSGS are more prevalent in TNBC than other breast cancer subtypes and occur at higher-than-expected frequencies across TNBC samples within TCGA. MSSGS may denote genes that play as yet unrecognized roles in cancer progression.

clinical trials↗