The complexity and variability of biological data has promoted the increased use of machine learning methods to understand processes and predict outcomes. These same features complicate reliable, reproducible, interpretable, and responsible use of such methods, resulting in questionable relevance of the derived. outcomes. Here we systematically explore challenges associated with applying machine learning to predict and understand biological processes using a well- characterized in vitro experimental system. We evaluated factors that vary while applying machine learning classifers: (1) type of biochemical signature (transcripts vs. proteins), (2) data curation methods (pre- and post-processing), and (3) choice of machine learning classifier. Using accuracy, generalizability, interpretability, and reproducibility as metrics, we found that the above factors significantly mod- ulate outcomes even within a simple model system. Our results caution against the unregulated use of machine learning methods in the biological sciences, and strongly advocate the need for data standards and validation tool-kits for such studies.

Bacterial infections are one of the most common causes of sepsis. If not treated quickly, sepsis can result in organ dysfunction, organ failure, or even death. In the presence of bacterial sepsis, it can be challenging to determine whether the infection is caused by Gram-negative or Gram-positive bacteria, which is essential for appropriate and timely treatment. Signaling molecules including cytokines and chemokines can be used as biomarkers of infection and evaluation of the host immune response. Human lung epithelial A549 cells were exposed to various Gram-positive and Gram-negative pathogen associated molecular patterns (PAMPs) including lipopolysaccharide, lipoteichoic acid and Pam3CSK4. A549 cells were treated with these agonists for 24 hours and mRNA expression levels of 84 human cytokines and chemokines were measured to evaluate immune response. Over 30 biological replicates were evaluated for each group and untreated control samples were compared to treated samples to determine upregulation or downregulation of each gene. All three treatment groups stimulated a significant upregulation of five pro-inflammatory cytokines and chemokines: CCL5, CCL20, CCL22, CXCL8, and LTB. The level of expression varied across all three treatment groups, highlighting the diverse cellular responses to three different types of molecules. These immune profiles reveal significant biomarkers of bacterial infection and can potentially provide a technique to quickly diagnose sepsis. This work was supported by the Defense Threat Reduction Agency (DTRA) to H.M. and C.A.M. (Award #CB11015). Cytokines and Chemokines and Their Receptors (CCR)

New and emerging pathogens are a critical threat to human health and biosecurity. As evidenced by the impact of COVID-19 and the subsequent threats such as avian influenza and monkeypox, there is a growing need to prepare ourselves to identify and address new and unanticipated pathogens effectively. The use of targeted assays - which are ligand based, requiring pathogen characterization and reagents - delays our ability to respond to pathogens. Indeed, identification of the pathogen, development or reagents, assay development, manufacturability are all key elements that impede usability of technologies. Our team has been working on addressing some of these challenges via the design of pathogen agnostic, reagent-free, deployable diagnostics for early diagnosis of emerging infections. Our approach mimics innate immune recognition in vitro, thereby allowing for identification of conserved host-pathogen interaction processes, which is extensible to all threats - known and unknown. Using different spectral modalities from optical (PEGASUS) to hyperspectral (ProSpectral), we are working on development of tunable and tailored diagnostic platforms for expedient point of care decision making. Indeed, the realization of these technologies is possible because of the innovation in machine learning methods, allowing for rapid analytics. Work on both COVID-19 and innate immune markers will be presented. Taken together, our work paves the way forward for rapid and reagent free identification of pathogens at the point of need by mimicking innate immunity, and using a combination of spectroscopy and machine learning tools.

There is an urgent need for truly reagent free and agnostic sensing at the point of need for biological and chemical targets. Indeed, the COVID-19 pandemic has re-emphasized the significant need for truly agnostic diagnostics at the point of need. Whereas highly targeted and tailored diagnostics can help us address a known and anticipated threat, they do not prepare us against the next emerging outbreak. To address the significant challenge of developing truly agnostic diagnostics that can improve situational awareness and guide decision making, our team has looked to spectroscopy as an inspiration for the sensing modality; and innate immunity as an inspiration for the biological process and signatures. All biological molecules emanate spectral signatures along the electromagnetic spectrum, integral information from which can provide insights into processes or signatures more effectively. Therefore, we propose the use of hyperspectral sensing as a modality for accurate diagnosis and detection of signatures. With regards to the biology - innate immunity is a broad, agnostic pathogen sensing strategy that allows for the early recognition of all pathogens – known and unknown – with an associated response. Our team has developed strategies to mimic this response in the laboratory, culminating with the (ongoing) development of a data-science and machine learning enabled effort to unravel the complexity of the immune recognition (host cytokine and chemokine response), allowing for their ability to inform on categories of pathogens/disease. It is important to integrate this knowledge with a point of contact diagnostic approach in order to be able to translate the data into usable diagnostic information. To this end, we are working on developing a hyperspectral approach that measures these signatures without reagents from saliva samples in a few seconds. Charged by a back-end machine learning/artificial intelligence algorithm, our approach uses Pattern’s ProSpectral Sensor that measures signatures across various realms of the electromagnetic spectrum. Data on developing this sensing modality as a diagnostic in real time – challenges and advantages – will be presented.

Universal and early recognition of pathogens occurs through recognition of evolutionarily conserved pathogen associated molecular patterns (PAMPs) by innate immune receptors and the consequent secretion of cytokines and chemokines. The intrinsic complexity of innate immune signaling and associated signal transduction challenges our ability to obtain physiologically relevant, reproducible and accurate data from experimental systems. One of the reasons for the discrepancy in observed data is the choice of measurement strategy. Immune signaling is regulated by the interplay between pathogen-derived molecules with host cells resulting in cellular expression changes. However, these cellular processes are often studied by the independent assessment of either the transcriptome or the proteome. Correlation between transcription and protein analysis is lacking in a variety of studies. In order to methodically evaluate the correlation between transcription and protein expression profiles associated with innate immune signaling, we measured cytokine and chemokine levels following exposure of human cells to the PAMP lipopolysaccharide (LPS) from the Gram-negative pathogen Pseudomonas aeruginosa. Expression of 84 messenger RNA (mRNA) transcripts and 69 proteins, including 35 overlapping targets, were measured in human lung epithelial cells. We evaluated 50 biological replicates to determine reproducibility of outcomes. Following pairwise normalization, 16 mRNA transcripts and 6 proteins were significantly upregulated following LPS exposure, while only five (CCL2, CSF3, CXCL5, CXCL8/IL8, and IL6) were upregulated in both transcriptomic and proteomic analysis. This lack of correlation between transcription and protein expression data may contribute to the discrepancy in the immune profiles reported in various studies. The use of multiomic assessments to achieve a systems-level understanding of immune signaling processes can result in the identification of host biomarker profiles for a variety of infectious diseases and facilitate countermeasure design and development.

Emerging pathogens are a constant threat to global health and human life, as exemplified by the recent COVID-19 pandemic. The availability of rapid testing - including in home tests - was instrumental in facilitating return to our 'normal' way of life. Diagnostics - especially those that can quickly and effectively inform on the condition at the point of need, enhance situational awareness and guide decision making and mitigation strategies can greatly impact our ability to counter emerging threats, epidemics and pandemics. Yet, current diagnostics are largely targeted- meaning they are tailored to identify a limited repertoire of anticipated threats. For true preparedness, it is important that the strategies we develop are pathogen agnostic and can be readily used against any organism quickly. Our team has sought inspiration from the human innate immune system in order to develop truly agnostic diagnostics. Innate immunity is medicated by pattern recognition receptors that have evolved to recognize evolutionarily conserved signatures on pathogens - allowing for their early identification. These germline receptors bind with conserved pathogen associated molecular patterns – PAMPs – produced by all pathogens, and result in the development of an associated cytokine and chemokine response. Innate immunity is thus a universal biosensor. Drawing inspiration from this natural system, our team has developed diagnostic assays for all pathogens. This involved understanding of common mechanisms of host-pathogen interactions, which then lead to the design of curated novel assay modalities - membrane insertion and lipoprotein capture - for the detection of pathogen. Beyond assay development and characterization of the response, we have evaluated the feasibility of these methods to detect signatures in blinded clinical samples for a variety of diseases (tuberculosis, invasive Salmonella, Shiga toxin carrying E. coli, Staphylococcal bacteremia, and others) with excellent sensitivity as compared to benchmark assays. These outcomes and strategies for pathogen agnostic detection will be presented. Transitioning this platform for field use, our team engineered a portable waveguide-based optical biosensor platform with integrated microfluidics for sample processes. The platform named Portable EnGineered Analytical Sensor with aUtomated Sampling (PEGASUS, R&D100 2021). This platform uses single mode planar optical waveguides as the sensing element, and builds on a bench top instrument that has been developed at the Los Alamos National Laboratory. PEGASUS miniaturizes this benchtop sensor, with integrated sample processing, moving us toward the goal of developing a truly fieldable biosensor. PEGASUS uses a different, smaller waveguide mounting apparatus, miniaturized robust components, sensing hardware and software.PEGASUS integrates Catch-all, the first ever sample processing system capable of lipid separation with ultra-sensitive optical detection of complex bioanalytes at the point of need. a microfluidic device capable of centrifugal separation of serum from blood at the point of need with a system that is compatible with biomarkers that are both hydrophilic and hydrophobic. The cross-flow filtration device separates serum from blood as efficiently as traditional methods and retains amphiphilic biomarkers in serum for detection.Whereas the scope of targets is extremely broad, the platform still relies on the use of reagents, which complicates field use in some instances. Therefore, we are beginning to explore multi-wavelength spectroscopy as a tool for the measurement of complex clinical signatures - within seconds, without reagents. Preliminary data from this development will also be presented.

Early and accurate diagnosis of respiratory pathogens and associated outbreaks can allow for the control of spread, epidemiological modeling, targeted treatment, and decision making–as is evident with the current COVID-19 pandemic. Many respiratory infections share common symptoms, making them difficult to diagnose using only syndromic presentation. Yet, with delays in getting reference laboratory tests and limited availability and poor sensitivity of point-of-care tests, syndromic diagnosis is the most-relied upon method in clinical practice today. Here, we examine the variability in diagnostic identification of respiratory infections during the annual infection cycle in northern New Mexico, by comparing syndromic diagnostics with polymerase chain reaction (PCR) and sequencing-based methods, with the goal of assessing gaps in our current ability to identify respiratory pathogens. Of 97 individuals that presented with symptoms of respiratory infection, only 23 were positive for at least one RNA virus, as confirmed by sequencing. Whereas influenza virus (n = 7) was expected during this infection cycle, we also observed coronavirus (n = 7), respiratory syncytial virus (n = 8), parainfluenza virus (n = 4), and human metapneumovirus (n = 1) in individuals with respiratory infection symptoms. Four patients were coinfected with two viruses. In 21 individuals that tested positive using PCR, RNA sequencing completely matched in only 12 (57%) of these individuals. Few individuals (37.1%) were diagnosed to have an upper respiratory tract infection or viral syndrome by syndromic diagnostics, and the type of virus could only be distinguished in one patient. Thus, current syndromic diagnostic approaches fail to accurately identify respiratory pathogens associated with infection and are not suited to capture emerging threats in an accurate fashion. We conclude there is a critical and urgent need for layered agnostic diagnostics to track known and unknown pathogens at the point of care to control future outbreaks.

The successful isolation of mycolactone in a laboratory or from a clinical sample relies on proper handling and storage of the toxin. Mycolactone is a light-sensitive and an amphiphilic toxin produced by Mycobacterium ulcerans. The biochemistry of the toxin makes it unstable in aqueous matrices such as blood, which causes it to self-aggregate or present in complex with carrier molecules. This biochemistry also impacts the use of the toxin in vitro, in that it tends to aggregate and stick to substrates in an aqueous environment, which alters its physiological presentation and limits its availability in a sample. Glass materials (i.e., tubes, vials, syringes, plates) should be used when possible to avoid loss of mycolactone sticking to plastic surfaces. Dark containers such as amber vials or aluminum-foil wrapped tubes should be used to avoid photodegradation of the toxin upon exposure to light. Sample storage in organic solvents is ideal for mycolactone stability and recovery; however, this is not always amenable as multiple diagnostic assays might be performed on a single sample (such as PCR or ELISA). In these cases, samples can be stored in an aqueous solution containing a small amount of detergent to enhance recovery of the toxin, and in order to avoid aggregation. Therefore, the downstream manipulations should be carefully considered prior to sample collection and storage. Here we present considerations for the optimal handling and storage of mycolactone in order to obtain quality yield of the toxin for various research and diagnostic applications.

Enzyme-linked immunosorbent assays (ELISAs) are widely employed for the detection of protein targets due to their ease of use, sensitivity, and potential for high-throughput analyses. However, the use of ELISAs to detect non-protein targets such as lipids and amphiphiles is complicated by the physical properties of these molecules, which affects their association with functional surfaces and recognition ligands. Here, we developed a unique lipoprotein capture ELISA in which the natural association between lipoproteins and amphiphilic molecules facilitates detection of the target biomarker in a physiologically relevant conformation. An assay to detect the glycolipid lipoarabinomannan (LAM), a cell membrane component and virulence factor associated with Mycobacterial infections, was developed as a proof of concept.

Discovery of reliable signatures for the empirical diagnosis of neurological diseases—both infectious and non-infectious—remains unrealized. One of the primary challenges encountered in such studies is the lack of a comprehensive database representative of a signature background that exists in healthy individuals, and against which an aberrant event can be assessed. For neurological insults and injuries, it is important to understand the normal profile in the neuronal (cerebrospinal fluid) and systemic fluids (e.g., blood). Here, we present the first comparative multi-omic human database of signatures derived from a population of 30 individuals (15 males, 15 females, 23–74 years) of serum and cerebrospinal fluid. In addition to empirical signatures, we also assigned common pathways between serum and CSF. Together, our findings provide a cohort against which aberrant signature profiles in individuals with neurological injuries/disease can be assessed—providing a pathway for comprehensive diagnostics and therapeutics discovery.

Rapid, on-site diagnostics allow for timely intervention and response for warfighter support, environmental monitoring, and global health needs. Portable optical biosensors are being widely pursued as a means of achieving fieldable biosensing due to the potential speed and accuracy of optical detection. We recently developed the portable engineered analytic sensor with automated sampling (PEGASUS) with the goal of developing a fieldable, generalizable biosensing platform. Here, we detail the development of PEGASUS’s sensing hardware and use a test-bed system of identical sensing hardware and software to demonstrate detection of a fluorescent conjugate at 1 nM through biotin-streptavidin chemistry.

The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics.

Traumatic brain injury (TBI) is not a single disease state but describes an array of conditions associated with insult or injury to the brain. While some individuals with TBI recover within a few days or months, others present with persistent symptoms that can cause disability, neuropsychological trauma, and even death. Understanding, diagnosing, and treating TBI is extremely complex for many reasons, including the variable biomechanics of head impact, differences in severity and location of injury, and individual patient characteristics. Because of these confounding factors, the development of reliable diagnostics and targeted treatments for brain injury remains elusive. We argue that the development of effective diagnostic and therapeutic strategies for TBI requires a deep understanding of human neurophysiology at the molecular level and that the framework of multiomics may provide some effective solutions for the diagnosis and treatment of this challenging condition. To this end, we present here a comprehensive review of TBI biomarker candidates from across the multiomic disciplines and compare them with known signatures associated with other neuropsychological conditions, including Alzheimer's disease and Parkinson's disease. We believe that this integrated view will facilitate a deeper understanding of the pathophysiology of TBI and its potential links to other neurological diseases.

Viral pathogens can rapidly evolve, adapt to novel hosts, and evade human immunity. The early detection of emerging viral pathogens through biosurveillance coupled with rapid and accurate diagnostics are required to mitigate global pandemics. However, RNA viruses can mutate rapidly, hampering biosurveillance and diagnostic efforts. Here, we present a novel computational approach called FEVER (Fast Evaluation of Viral Emerging Risks) to design assays that simultaneously accomplish: 1) broad-coverage biosurveillance of an entire group of viruses, 2) accurate diagnosis of an outbreak strain, and 3) mutation typing to detect variants of public health importance. We demonstrate the application of FEVER to generate assays to simultaneously 1) detect sarbecoviruses for biosurveillance; 2) diagnose infections specifically caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); and 3) perform rapid mutation typing of the D614G SARS-CoV-2 spike variant associated with increased pathogen transmissibility. These FEVER assays had a high in silico recall (predicted positive) up to 99.7% of 525,708 SARS-CoV-2 sequences analyzed and displayed sensitivities and specificities as high as 92.4% and 100% respectively when validated in 100 clinical samples. The D614G SARS-CoV-2 spike mutation PCR test was able to identify the single nucleotide identity at position 23,403 in the viral genome of 96.6% SARS-CoV-2 positive samples without the need for sequencing. This study demonstrates the utility of FEVER to design assays for biosurveillance, diagnostics, and mutation typing to rapidly detect, track, and mitigate future outbreaks and pandemics caused by emerging viruses.

Multiplex biomarker quantitation is ideal for tracking the progression of a disease, but sensitive, specific, and quantitative multiplex biosensing remains challenging. We achieved total internal reflection with two lasers in a single planar optical waveguide by coupling 532 nm laser light into the diffraction grating of a waveguide and 635 nm laser light directly into the thin film of the same waveguide. We confirmed that the evanescent fields generated in the waveguide can excite two fluorescent dyes (Alexa Fluor 532 and Alexa Fluor 647) held to the surface of the waveguide through biotin-streptavidin chemistry. This sensing concept holds significant potential as a platform for multiplex biomarker detection.

Mycobacterium ulcerans is the causative agent of the chronic and debilitating neglected tropical disease Buruli ulcer (BU) which mostly affects children. The early detection and treatment of M. ulcerans infections can significantly minimize life-long disability resulting from surgical intervention. However, the disease is characterized by relatively few systemic systems as a result of complex host-pathogen interactions that have yet to be fully characterized, which has limited the development of both diagnostic and therapeutic approaches to treat BU. In this work, we study the interactions of the host immune system with two principle M. ulcerans virulence factors: mycolactone, an amphiphilic macrolide toxin, and lipoarabinomannan (LAM), a cell wall component of most mycobacterial pathogens. We observe that human lipoproteins have a profound effect on the interaction of both mycolactone and LAM with the immune system. Individually, both molecules are pro-inflammatory in the absence of serum and immunosuppressive in the presence of serum. When combined, mycolactone and LAM are immunosuppressive regardless of serum conditions. We also show that Toll-like receptor 2 (TLR2), a macrophage pathogen pattern recognition receptor, is critical for LAM immune stimulation but aids in mycolactone immunosuppression. These findings are a first step towards unraveling mycolactone-mediated immunosuppression during BU disease and may facilitate the development of effective diagnostics and therapeutics in the future. Author Summary Buruli ulcer (BU) is a neglected tropical disease caused by the pathogen Mycobacterium ulcerans . The principal virulence factors associated with it are the macrolide toxin mycolactone and the major cell wall component lipoarabinomannan (LAM). Here, we examine the impact of the amphiphilic biochemistry of mycolactone and LAM on their interaction with the human immune system. We show that both mycolactone and LAM associate with serum lipoproteins, and that this association is critical for the immune evasion seen in early-stage M. ulcerans infections. In the absence of serum, mycolactone is pro-inflammatory. Immunosuppression occurs only in the presence of human serum lipoproteins. In the presence of LAM, mycolactone is immunosuppressive, regardless of serum conditions. Immunosuppression is a hallmark of BU disease, and understanding the mechanisms of this immunosuppression can support the development of effective diagnostic and therapeutic strategies.

Diversifying our ability to guard against emerging pathogenic threats is essential for keeping pace with global health challenges, including those presented by drug-resistant bacteria. Some modern diagnostic and therapeutic innovations to address this challenge focus on targeting methods that exploit bacterial nutrient sequestration pathways, such as the desferrioxamine (DFO) siderophore used by Staphylococcus aureus (S. aureus) to sequester FeIII. Building on recent studies that have shown DFO to be a versatile vehicle for chemical delivery, we show proof-of-principle that the FeIII sequestration pathway can be used to deliver a potential radiotherapeutic. Our approach replaces the FeIII nutrient sequestered by H4DFO+ with ThIV and made use of a common fluorophore, FITC, which we covalently bonded to DFO to provide a combinatorial probe for simultaneous chelation paired with imaging and spectroscopy, H3DFO_FITC. Combining insight provided from FITC-based imaging with characterization by NMR spectroscopy, we demonstrated that the fluorescent DFO_FITC conjugate retained the ThIV chelation properties of native H4DFO+. Fluorescence microscopy with both [Th(DFO_FITC)] and [Fe(DFO_FITC)] complexes showed similar uptake by S. aureus and increased intercellular accumulation as compared to the FITC and unchelated H3DFO_FITC controls. Collectively, these results demonstrate the potential for the newly developed H3DFO_FITC conjugate to be used as a targeting vector and bacterial imaging probe for S. aureus. The results presented within provide a framework to expand H4DFO+ and H3DFO_FITC to relevant radiotherapeutics (like 227Th).